Elaine_Zhang__MSIS522_Assignment3.ipynb

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      "name": "Elaine_Zhang _MSIS522_Assignment3.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zK7EWLIXRu0C"
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      "source": [
        "# Clustering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Bq6HcaJSzWe"
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      "source": [
        "## Problem 1: Segment Customers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5dKNjMuKRu0E"
      },
      "source": [
        "The problem that we are going to solve in this assignment is to segment customers into different groups based on their shopping trends."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "snzfWDzvRu0F"
      },
      "source": [
        "# import packages\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set()"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O5K8-XTXRu0K"
      },
      "source": [
        "### Load data\n",
        "\n",
        "Our dataset has five columns: CustomerID, Genre, Age, Annual Income, and Spending Score. To view the results in two-dimensional feature space, we will retain only two of these five columns. We can remove CustomerID column, Genre, and Age column. We will retain the Annual Income (in thousands of dollars) and Spending Score (1-100) columns. The Spending Score column signifies how often a person spends money in a mall on a scale of 1 to 100 with 100 being the highest spender."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbOcjmnqRu0L",
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          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "21fb5428-bb28-494b-e02d-186ada9d3dc1"
      },
      "source": [
        "# load the data\n",
        "shopping_data = pd.read_csv('https://raw.githubusercontent.com/zariable/data/master/shopping_data.csv')\n",
        "shopping_data.rename(\n",
        "    columns={\n",
        "        'CustomerID': 'customer_id',\n",
        "        'Genre': 'genre',\n",
        "        'Age': 'age',\n",
        "        'Annual Income (k$)': 'annual_income',\n",
        "        'Spending Score (1-100)': 'spending_score'\n",
        "    },\n",
        "    inplace=True\n",
        ")\n",
        "display(shopping_data.head())\n",
        "\n",
        "# TODO: retain only anual_income and spending_score for clustering\n",
        "shopping_data_clustering = shopping_data.iloc[:,3:]\n",
        "#display(shopping_data_clustering.head())\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>genre</th>\n",
              "      <th>age</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>spending_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customer_id   genre  age  annual_income  spending_score\n",
              "0            1    Male   19             15              39\n",
              "1            2    Male   21             15              81\n",
              "2            3  Female   20             16               6\n",
              "3            4  Female   23             16              77\n",
              "4            5  Female   31             17              40"
            ]
          },
          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wx6deWgoRu0O"
      },
      "source": [
        "### Hierarchical Clustering\n",
        "First, we will apply hierarchical clustering and use dendrogram to help find the number of clusters within the data.\n",
        "\n",
        "**TODO: Use dendrogram to plot hierarchical clustering and find the number of clusters that makes sense.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11EVF7NPRu0P",
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          "base_uri": "https://localhost:8080/",
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        },
        "outputId": "1030080a-c577-47d6-f44e-c5d8a2d3dc75"
      },
      "source": [
        "# TODO\n",
        "import scipy.cluster.hierarchy as shc\n",
        "plt.figure(figsize=(10, 7))  \n",
        "plt.title(\"Dendograms\")  \n",
        "dend = shc.dendrogram(shc.linkage(shopping_data_clustering, method='ward'))  \n",
        "# 5 clusters "
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_8vcGNyRu0T"
      },
      "source": [
        "**TODO: Apply hierarchical clustering based on the number of clusters you pick from the dendrogram, and visualize the results using scatterplot.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDz8vl9rRu0T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "62dbbf94-ad0d-499c-ad7f-6bb814b346b3"
      },
      "source": [
        "# TODO\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "\n",
        "hc = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')  \n",
        "y_ward = hc.fit_predict(shopping_data_clustering)  \n",
        "\n",
        "plt.scatter(shopping_data_clustering.iloc[:,0], shopping_data_clustering.iloc[:,1], c=y_ward, s=50)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbcce4c32d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zc1jqvW7Ru0W"
      },
      "source": [
        "### K-means Clustering\n",
        "Then, we will apply k-means to the same data and visualize the results.\n",
        "\n",
        "**TODO: Vary the number of K from 2 to 10 and plot the Sum of Squared Error (SSE) as K increases and pick up the value of K that makes sense.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gC5iFpoRu0Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "da1568c3-501b-4edd-d34d-a45834f4e591"
      },
      "source": [
        "# TODO\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "sse = []\n",
        "for k in range(2, 11):\n",
        "   kmeans = KMeans(n_clusters=k)\n",
        "   kmeans.fit(shopping_data_clustering)\n",
        "   sse.append(kmeans.inertia_)\n",
        "\n",
        "plt.plot(range(2, 11), sse)\n",
        "plt.xticks(range(2, 11))\n",
        "plt.xlabel(\"Number of Clusters\")\n",
        "plt.ylabel(\"SSE\")\n",
        "plt.show()\n",
        "\n",
        "#based on elbow point, 5 clusters make the most sense"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "52XDinaTRu0b"
      },
      "source": [
        "**TODO: Cluster the data using K-means based on the pre-defined value of K from the previous step and and visualize the results using scatterplot. **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWQ4KJp-Ru0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c46ddd02-bb5c-499d-c4cd-89b4ce405e71"
      },
      "source": [
        "# TODO\n",
        "for k in range(2, 11):\n",
        "   kmeans = KMeans(n_clusters=k)\n",
        "   kmeans.fit(shopping_data_clustering)\n",
        "   y_kmeans = kmeans.predict(shopping_data_clustering)\n",
        "   plt.scatter(shopping_data_clustering.iloc[:, 0], shopping_data_clustering.iloc[:, 1], c=y_kmeans,s=50)\n",
        "   plt.show()\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C9JQAoNmicKcMQQtLPHfD6wAtLAE1og1SaSaCfvpddEHCk9gH5OaxV9GUafFZPY2Frut4Sh0oBjF5k4Zh1LSJXP/Mn+nQryv28BBiUxK45E+/5Y//vr+SdMMnT77Nz4uW4i73qXUqHoUdP2zk1VueQjmj8YrH6cZZFlzdFQjofOubvmOHIMqB/6RlWabf+GF+x8WyHOwZK5HcxQi6jqDrSK4iQtJXIJY3kga/IODqOAp38iA0awS6aEENicXVYSTeJP+wm8m5Y87464FjP25mx4uf4cwuAiB+cHcGPTKNiE6mBkpj0HNkP3qO7Gd4vuBEHtu+2+AXG1cVleLsAnYt30Ri57Z8MXcRR3YcAEGospDKaFO5PknolMywKRfw69erzlrh2Bg4aThtuvqHSGyZmyvNqk8h6Cq2zK04uzVS/wlBRInvjhJvJi3UB6bjr2OOfLOBzU8uqrQBm7txH8tvmMv4Dx8lrG1cI1pnEogDG3YZFmt5nG5+/XoVh7bvx+Oofk58XVfYel0edHSsVaT1Tn3oOtr36cKKd5ZQlFVAVGIMF95wKUOvuCCQkYhOY40o0ZFr9tptoZiOvw7RVY3tL3xSyen7TugoTjf7Fi5j8N+nN45xJoaIkhQ0+yfnUGbADJ5g40kGgmvnyrE9h/jqufc5uisdgDbd2jPlwel0HtQj4PWCIDDk8lEMubyKFqGaijXTWHbi5Gg1sNikOWDG+OuQ0sPZPlmFAOiqRuaK7Q1skUl16DWqv2FM3hpio7yo1LASNxBJXdsSFl17+YwT+47wn9v/yeHtB9BUDU3VOLHvMG/d8zwZW9JqPrCuYz+0EkvBAUPXrgNqRFtDgcNT46Ap5qqgGWI6/jpEkEQCtSytOG+k/WLSqITHRnLRjZf75fVbbFba9e5cfS18wZcxdNXDN9SJXd+8/FHgWgG3h6+e/6DG44qOPKTyvICxfTj5ERYtuJMNNIE0FWvmVsJ2fULYzk8I2/0ZluydoLcyAblmjOn465DwDolYDRpuixaJdpOGNrBFJtVl4u1X8tt/3Exi52Qki0xEfBTjbpnMLS8/SJ+xQww3a+3hIdhC7cg2Cz3P78/dbz5Kx/51U1dx8Ne9hueyDhwNqjUUDLnkBARx+potEkf3Sej2AHUIuo49YwWWvDSfhg46gurBmrMb25E1NbLHpOExY/x1iCAIDH7sWtb99bVKTVIEScQSGUaPGyc2onUmVTHw4uEMvHi43/GJt01l98rNOEsdlUI+FruVP75wv2G8vbZUpa4g1DQGH3RgASW6I7otcKhKKs9Bchb4rRYEXUUuOYHXVYxmyik0ecwZfx3TZlQfRr96L3GDuiHIElKIjY5XjGD8B3/HHqDlnknTJ7pNHPe+N5uBF4/AYrciyRLdhvXmjv887Of0Na3uwh09R/Y33HRu36dLjbtyKZHtQDDYfBZE1Mh2hvdKJceNtfF1DankRI1sMjmJrjfInok5468H4gd146K3HmxsM0zqkJjkeK6ZE7jKWtd11nz0IyveWUJJbhH28FDO/+1Yrnnk2lo989J7fsfBX/fgcborJJ/Bt9KY8mDNs8O00FiUyLbIJSf8KmOVyBS0kOgajiyYiUA1RfFgzdqKpfAQ6Cq6NQJ3m/6o0cbaTLXBnPGbmNSSL59dxLfzP6Yk11ew5ypz8MsH3/H8DXNqtQJI7JTMn9+ZSeoFA5FkCVES6T68D3e/+Sjteneulc3uDiPxJPVDk+2+uL4cgqdNf9wdgusBKVHtjRuhCAJKhKmjc85oCqEHlmEpyEDQVQRA9JRiP7oOS67xPk9tMGf8dYTidLP7tSUc+nwV3jInYSnxpN5xOR0vN5bRNWn+FJzIY+OXvwRUxDy8M4MDG3bTY0TfGo+f2CmZG5+/t7Zm+iOIeBNT8SamntNtWmg8amgiUnmO/2ohun3gDWGToMgF6QheJwKVJwmCrmLN3o43rhuIdeuqzRl/HaB5VVbe+jwHP1yOt9QJOpQfy2PLU/9lz+tLGts8k3pk35rthpulbocraFP2Zokg4Oo8Bk9CKrpkPblasONp0w93O3OSUxMsRYcNU2tBRCr378FdW1rMjL9w92F2vvwFuZv2I0gibS8aQN97rmwQiYQTy7dSeigHzXNWn1SXh71vL6PrtIsCpnnmbkpj58tfUrjrEJLNQrtJQ+lz9xTsceasqdmgE3QzrkWmtgsi3jb98Lbp53v3ZtDcpWlT1WZu3W/2togZf/62g6y89QVy1u9FV1Q0t5dj32/ixz88TfmJ/Hp//pFvN6AaVOyKskj2ugA9dH/eweo/z6dgezq6qqE43Bz+ah0/Tn8ad1FZfZtsUkf0ON84jGMLtdNv/LlJKzc7TKdfa5SojuhGWVa6hhqaUOfPrLXjX758OVdeeSVTp05lypQpfPfddwBkZGQwbdo0Jk2axLRp0zh06FBtH2XIln9+iHp2haOm4y13svs/i+vtuaeoqpxfVyuf1zWNzU+9XynX33ediqe4nP3v/1jnNprUD3HtEhk4aYRfaqVstZDSsz09RhirgpqYAHhju6DLdvSzUqJ0QcKT1Bekuu+uVyvHr+s6M2bMYO7cuXz55ZfMnTuXhx9+GE3TmDVrFtOnT2fZsmVMnz6dmTNn1pXNlXAXlVGakRn4pKZzYvnWennumbSbOBgpJLBqoqaoJI6o3PWoNCMLb5kr8PUehaPfbqxzG03qj9889kcm3DaV0OhwBFHEGmJj+NUX8tAHsxtFntmkmSFZcHS/2Fc4J4joCGiWUNwpQ/Em9q6XR9Y6xi+KIqWlpQCUlpaSmJhIYWEhu3fvZsGCBQBMnjyZOXPmUFBQQGxsbG0fWQld0wiWPKwbpNNpXpX9H/zIwQ9X4CkuI6xdAqm3XUa7iee+NG83cQh7316K43gemvf0Jo1kt9L5N6P9Crd0VQuufVXHkr4m9Ysoilx0w2VceP2lKG4vklVGFEVsITYoMxvxNCaiowBr1nak8hyckoQ1sgPepL7olpDGNq0ysh13h/Nx68N9G0OCVK9htFo5fkEQePHFF7n77rsJDQ2lvLyc119/nczMTJKSkpBOtp+TJInExEQyMzPr3PHbYiIIbRNL2dEc/5OiQJuR/h17dE1j9X2vkL/1QIWEcsmBE/w6611KM7JIvf3yc7JBslkY+84Mtj//CUeXbkTXdKyRofS8aRLdpo/zuz6iSzKiRQb89wUEWaLtOANxLJMmjSAINa6mNal7xLIcQjJWwMnceBQVS8FB5JJjOHtcit4UWzgKou+/eqZWjl9RFF577TVeffVVhgwZwqZNm7j//vuZO3dunRgXFxderevGPHUj390+D+WsOL9stzL60WuISaisO3J05Q4Ktqf76earLg/73l7KsDsuJST+HPVGEiJIeeVPaIqK4nRjCQ8JqvE+cua1rHpsYaXWjIIoYAmzc/4DVxGeUDNZ34Qa3tfUaA7vUZRTyNcvfcyGxWtQFZU+Fwzgyr9MI6V7+4prmsN7VIfm9h66ruM+8A362ZpCJ0XlIksPYO0RvFitKVPb30etHP+ePXvIyclhyBBfeGTIkCGEhIRgs9nIzs5GVVUkSUJVVXJyckhOTj6n8fPzy9CqoYMe0rcLw5+7g+0vfEzZEV/XoNj+nRk4YxpKdCS5uaWVrt/x4QoUo25KksTOL9bRaerIc7LVD1fwzJy4sYMYPEtj50uf48zxVXwmDOvJoIen4ZQtOM+yuTokJET4vWtzpDm8R0leEfOmz8JRUl6h5b956Xp2rNjCHf95mHa9OzeL96gOzfE9BE85oc6ywEFgXcObuZ/imJoX1jUmwX4foihUa8JcK8ffpk0bsrKySE9Pp0uXLhw8eJD8/Hw6duxIamoqixcvZurUqSxevJjU1NQ6D/NUsmVkb9qMnIWnqJzD36wn/aOVrLz1BcI7JNLr1ktJGXs6fHJ2vn0ldA1NaZhG2e0vHkK7iYPxljqQrBYkM0xQr5QXlbJ84RK2fLsW1avQdWgqE26fSnK39lXffBbfv/4FjtLySg1cdF3H43Tz+b/e5Z53Z9Wl6SbnShUFFEKLLLCoPrVy/AkJCcyePZv77ruvIqzx9NNPEx0dzezZs3nkkUd49dVXiYyM5JlnnqkTg4Oh6zqbnnqf7NW7KtI7i/YcYeOjCyi95RJ63XIpAMkXDeDYD5shwGpC86okntfL73h9IQgC1sjAGv4mdUd5URnzrptNWUEx6skN+F0rNrNv7Q5ue+Whc9bQ3/7DRsMJQuaBY5QXlTW78EhLQreGo0sWhAC/Ix0BJaJtI1jVdKh1Vs+UKVOYMmWK3/GuXbvy8ccf13b4cyJ/ywGy1+zyy+lXXR52vfIVjuxC+tw1BWtkqGG1pa7pWCKa2I6/Sa1Z8e4SygpKKpw++CYKXpeHT59awF/+99Q5jRdsVSgIAqoSZFVpUv8IAp7kQdiObaisKQQgnsyPb8W0qCTjI99u9C/kOoNDn6/mh2ue5PBXaw2roEWbTNYvO+rJQpPGYsu361C9gZ1x/rFcirILzmm8YCuEkMhQIuLMZiSNjRLTCXe74WiWUPST2TJaaBzOrhMMG820FlqMVg+A6nIHlbXQVQ13YRmFe44YXiMAqrtpzdaK9x9nzxtLyNt0ACnUSucrR9Nt+lhkg6IxE39Ur9fwnOLx8tVz7zP9qbuQrdX7kwiJMA7PWUPsQTO6zmbPL1tZvnAJeUeyiEqKZcx1l2ANtbNi4RLyj2YTlRTLhTdcxoCJ553TuCagxHREie6AoLiIS4gkr8j4c9CaaFGOv83ofpxYvs04YwfQFRVXXglSiC2gvo6uQ8Kw+mmlVxNyf01j9b2voHq8vj2JQtjz5jccXbqRse/OMJ1/Nel2Xh92/LjRUF5j14rN/GvqX/n7khcQxaoXwvvX7zQ8V5SZh6O4DKoR4//xra9YvnBJRVP18qIyPpr9FqCjnSzkKy8q49MnF5C+eR9XP1I3jdxbFYKAbglBsNgB0/FDCwv1pIwbiD0+Cqook9d1HXtsBIJcWRhJtFlIHt2XiI5J9WlmtdF1nY0zF/rCV2c4LM3tpfxYLhmf/NKI1jUvJtw2FYsteNZUaV4xvyxaVuVYBzbsxlXuNDwvSKKfPn8ginMK+entryuc/ik0Va1w+qfwON1sXryazP1HqxzXpG4QXCXYjqwldPcXhO79GkvOLlBbxhdHi3L8okXmogUPkXxBkI0bARKH9mDsuzNoM6oPokVGDrUh2a10uXo05z19c8MZXAUlB0/gKS4PeE51e8n4YnUDW9R8Sercltv/bwahAeSxz2Tdpz8FPb/6f9+z8MF5QYX5wqLCiYivun3hrhWbzqksX/F62bpsXbWvN6k5oiOP0APLkIsOIypORE8Z1uydhBz4rkU4/xYV6gGwxYQz8oW7yPh8FVuf+QjtrJmXZLPS+64rsMVEMPLfd+EpdRAuglOSA+bR65rG0aW/sv+Dn3BkFyBZZDSPgiUilI5TzqfbtAuRQwOXfuuaxpFvNnDgg59wFZQSk9qBXrdcSmzfTpWvUzUOL17HgQ+X4ykqI6Z3R3rdcim6riMECTucXXlsEpz2fbrQa/RANi8x/sIszi3iqcv/QmR8NBdMv5gBFw+viKuXF5XxzUsfB53NyzYLl937+2rF4r1uL5pS/XxyXdNxBwljmtQdtqPrEM5qKi/oGqKnHEteGt4kfymY5kSLc/yn6HzVaERZZse8z1CcbnRNJ7RNDIMfu46Y1NMNjK0RoUQlROAJUAmn6zobHltA5srtqM7Ky3F3QSl7Xl/C4a/WMu69h7GEh/jdu27GG2Sv3V1xb2ZuETnr9zJk1nW0nzTMd52msebB/5C7cV/Fdc6cYrLX7mHwP641nFkKkkjSyPpR7mvJDL5kRFDHr3oVSnIKKckp5NMnF7JvzQ5+P/tWBEFg989bqlTbFBBo27NjtWzpMrgnkkWqVAQWDGuonR7Dm7fDaQ4I7jJEjyPwOV3FUphuOv6mTMcrRtDh8vMoO5aHKEuEtY3DkV3Ituc+IvPnHYgWmY5XjCDqrsCibHmb9gd0+qfQ3F4cmfmkLfqBPndeUelc9prdZK/dU/le3VdTsPmJ92l74QAku5XMX3aQuzHtrOt0VJeHLU//lyk24bAAACAASURBVB43TCDtne/90lQlm4UeN06s2Q+mFdN9RF8i4qMpzSuq8lqPy83mb9bgdXuYfP81eF1uv9j72Xg9Hj59aiF3vfG3Ksdv17sz7VI7cXRXRpV7ApIsEZ0UQ6/RA6oc16R2CJriC8EZRfO0ppX1VxNaVIw/EIIoEtEhkbC2cZSkZ/LD7+Zw8KOfKT+WR2lGFrtfW8Knl/3D1yv3LA59uSZoXQD45B8Of7nW/94vVht25UIUyF6/x3fd50GuA2L6dCT19suQw+zIoTZEm4XIbm0Z88ZfCG9X9515WgN//fgpEjtXXzdqx0+b+Pcf/kFkYgyCWEUIR4ejuw7iKAm8N3MmgiBw87y/0G/8UGSrBVuYHdkq03NkX/qOG4JslU8es9BjZD/ufP3vpr5/A6DZIjCSetcRUMObRvJHbWjRM/6z2TxnEd5yZ6Vvcs3tpfR4HnsXLKXfvVdVuv5U4/SqUAI4bm+ZcdYHuo5S7j55XeCGLCcvRHV66PnHSXS/djxlR3KQQ+2EJtef5lFrwB4eyoMfPU1JbiHpm/fx69er2L9ht3HvXF3HVe5k5bvf0mlAdzK2pAWdoQui6JepY4Q1xMY1T9zO1Ieuoyg7n8j4aMKifWmgzlLHyWMxhEVXT6nWpA4QJTyJfbBm7/Bvgi6IeBKbd5gHWsGM/xTuojIKdx8O6Mg1j+Kr5j2LpFG9kUKqEE4TBBKG+Of9txnZx1B0TVNU4gZ29T1jZG9EW+DWappXJbZ/F8CXsRTZta3p9OsQW1gIP729mIzN+4I2TAdAh+N7D/Pbx26i/4RhwXr/EBIeQsQ5ynqHRISS3K19hdOvfMx0+g2NN6EXnqR+6KLs+0+Q0KzhOLuMRbc3/6rsVuP4VacHQTJoaAwBQzodLx/uy9gJsryXbBZSb7/M/94rR/oc/1n3SjYLyRf2J6xtHACdrx6NZLP4pfWJNgsp4wcRmhQT9L1Mas7Pi5aSfywHxUDK4WxESUTTdKY9fhu3vPQgstX/C9titzLprt9UqwjMpAkjCHgTUynvfTXOrhNw9LgUR8/JaGEtI7zaaj6dIYnRSHbjpsWnZtZnIofaGfvODGL7dkKwyAhW3xeHIEtIdiuhybGMnHc30T39ZX2tEaGMfWcGMb06INosyGF2RJuF9pedx7A5f6y4zhYdzth3ZhDdqz2SzYIc7ruu4xUjGDrbrNKsTzZ8+XO1Cq1OYbFZiU7yrbh6jOjLDc/eQ2RCDNYQG7ZQO/bwEC679/cMmzqmvkw2aWhECS0kxqft04LkMgRdr2qN23hUtxFLdTn4v5XsmPeZ3+xetlsZ/Z/7iAvg/E/hyCzAVVBCSFIMrpwiJLuViM5tqpWvXX4iH3dhKeEdErFGhBpfdzwPd1EZER2SaqQQ2hwbZgSiod5j1tg/4SoLnLZ3Nha7lUvv+R2jfj+h0nFd18k+eBxVUWjTrR2SfHrbrC7ew+1wseHLn9nyzRp0Xaf/hGEMv3osoQ0o5R3oPcSyHKx5exHcZWj2SLwJqWihcQ1mU01oDX8fDdKIpbnR5fdjUD0e9ry2xKffoelYwmyMff52QvsZO32A0OTYivj6ubZlDGsbVxHaCXpdSjxhKfHnNLZJzenYryv71hoosQpgtdsQRAFdhwm3TmHk78b7XyYItOnWrl7scxSX8fKNT1CaV4TX7VuZ5BzKZPX/fuDed2cRmdA4YUBL9k6sObsretmK7mLkkhO42w5CieveKDaZnButyvELgkCP6yfS9fcXUbz/OKJFIqp7ColJUS1iJmBybky840rSt+zzy8Cx2K1MvONKup/XB8XjJbl7BywGG/D1ybL/fEZxTkGlHgKK20uZUspXz33Adc/8qUHsUIuzsR3ejOguRZNDkcuzKnWwEgB0FduJzShR7aEpNjE3qUSrcvynkGwWP9kEk9ZH+z5duO5fd/PxE2/7nL8goGsaY2+8nDHXXtLoEsibl6yp5PRPoasau3/ZguJVkC31+ydsydmDJ2cnsqb4ZvcEK3wTkIuPmrP+ZkCrdPwmJqfoNWoAj377b07sPYzX4yWlV0es9qYhde11G9cC6Jpv9l+fjl/wlGHN3lER0oGgWay+69Tq1S+YNC6m4zdp9YiiSLvenRvbDD+SOqeQdfBYwHMRcZHYwuo3pCIXHqq6vuFMRBktpGlv8Jr4aDXpnMFwF5eT9t4PrLj5OX65ax5Hl25EC7DENjFpSCbd/RssAYoALXYrE++8qt5DUYLiRiCwNtHZXwc6ApoltEXIGbQGWv2M35FZwDd/nIun1FEhc5y/PZ0DH65gzGv3+4qrTEwagd5jBjLlr9ey+N//PXnEtwcx8fYrGXbFBfX+fC0sHr0w3U+e+BS+PrYS6BpqSCzuTqNbVK57S6bVO/5NT7yHq6AU/QzVRdXpoWjfUfZ/8BO9bprUiNYFwVWOZd9qpON70a0hKD1Hosef19hWmdQx500dw+BLz+fIjoNomkaHfl0bbA9CiWyHVdyCoKmcOcfXEdCt4Tg7XoCoONCs4a2+eXlzo1U7fk9xOXmbD1Ry+qfQ3F7SP17ZJB2/UJRNyOJ/g+pFONkNSDqxD/fhX2HU9SCYEbyWhGy10GVIr4Z/sCjh7DaR8GOr0cqLfJ8rXUMLicbV8QJ0SwgqzV+3pjXSuh1/qQNBFg37LweSam4K2JYvAI+jUoaFoHjQMnYiJW9C7Tqs0WwzaVno1jDsw39D/tGjCJ5yNGsEuj2ysc0yqSWtemoYmhQTtLVhdI/6qcisDUJxDmJJbuC0Oq8by66fG9okk1aAZo9GjUwxnX4LoVXP+EWLTPdrx7P/vR/8NPUlu4XUOwJ35mpMBFcZiJJhw2fBZVYgtwQKTuSx9pMfydp/jPiObTj/t+NI7FT95jEmJsFo1Y4fIPX2y5A8HnZ/sBzxZDGMrukMfHgaiec1Qly1CrToJOPWb4KAllC9fq8mTZddKzfz30dfQ1NVVEXlwMbdbPxiJVMfus5U/jSpE1q94xdEkdFzbqTT9RPJ23IQySaTMKSHYROVRscWhtJlCHL65oqN3QokC97+Zh/e5oyzzMF/H3utUtWupmpoqsYXcxfRfUTfCmloE5Oa0qpj/Gdii4kgZdxA2ozq23Sd/kk8I3+P2qEvuiSjW2zoFju6xY71ijvQ4prevoRJ9dnx469BCrN0Nn+zpkHtMWmZtPoZf10iFGYi71uDWF6EmtARpcf5YK8H3XTJgnv0dORdK5GP7kC3huLtP4HQ7oMgr6zun2fSYJTlFxtq9CgeheLsgga2yKQlYjr+OkLe/gPWLd+CpiLoGtKx3Vi3LcN1yZ/rPO4ulOZjX/xvBK8LQfGgIyBlH8RzfAQM+Y1ZPdmMadOtPVa7DbfD5XfOGmKjXe9ODW+USYvDdPx1gJh/DOuWbyvF3AXVCyrYvn8N5zVPQh32YLUtX4jgKkU4KaAloIPiQd2zHim+O2qnAQgFJ5DT1iI6StAlCUFV0EMiULqPQIv3bxXZ0OzdsY/vv/yestIyhowcwoWTxmBrIqqYjUmvUf0JiQjF43RzdnM8ySIzYOLwRrLMpCVRa8fvdrt5+umnWbt2LTabjYEDBzJnzhwyMjJ45JFHKCoqIjo6mmeeeYZOnTrVgclND3nvKsNMG0H1ImamoaXUTYaQUJqPWHiiwulXwuvGsnM5Yv5RLDuX+yp78RXbC4AuCMhp6/D2HIl3+NWNsjLQdZ0XH5/HT0uW4/F40DWdtSvWseClhcx7/0US27SMZtY1RZRE7njtEd6653lK8nza94IgYAuzc/NLf8EaYn45mtSeWjv+Z599FpvNxrJlyxAEgby8PABmzZrF9OnTmTp1Kl9++SUzZ87k3XffrbXBTRGhND+wIwbQdURHsYHGYQ2e5SgOnsdfmoul4Fjl1cep/+s6qF4saWvRUnqhtu9z+kbFg5yxBTEzDdxOxJJcUBW0tt3xDJ1aZ3sVv3z3C8u/WYHbdbpuwuVw4XF7+NfD/+KFd56vdH3arjR++PpHykvLGTp6KKMnjMJi8QnnlZeV8+Pin9izbS8JbeK55OpJtG3ftk7sbExiUxL466f/5NC2/eQdySYmOY4uQ3oh1uGq0aR1UyvHX15ezhdffMHKlSsrMhHi4+PJz89n9+7dLFiwAIDJkyczZ84cCgoKiI1tealoWnwH9KyDhiqGWkzdFd5okQnGefwACKAEb4YhKB7kncsrHL9Qkod9yYsIXjeC4q6Q4xIAMS0fOW09rsvuQWvTrdb2f/ruZ7ic/vFrTdVI25lGTlYuCQkRvpXB7Hn89M1yvB4vmqax6ofVvD1vAfMW/ZvC/CIeumkGiqLgcrqQZZnP3vucW+6/mauuu7LWdjY2giDQeWAPOg/s0dimmLRAauX4jx49SnR0NPPnz2f9+vWEhYVx3333YbfbSUpKQpIkACRJIjExkczMzHNy/NXpFl9XJCTUXF1QGzUJ156f/R2yICLGJhHbq3cdaqdH4Oo2CO3AVv9Zv2xFsoegO0uqHMXiKiby5Ds7v34W3VVa0XSjkgYQADoh3/0foQ++XivLnQ4Xx4+cMDxvtVnRvD59pM1rNrBiaeWVgdPhxOPx8MLM58nYf5iy0tMZTIqigAJvz1vARZPOp0fv019S5WXlfPPpd+zetpektklMveYyktu1qdW7VJfafK6aEuZ7NC1q+x61cvyqqnL06FF69+7Nww8/zLZt27jzzjuZN29erYw6RX5+GZp2Dh2AakhCQkQtm61bEcfdiv2ntwAdNM3XvzU8Fte42ymr6xTL836PraQYKSfD9++TqonWidfhOLAbuSi3UjPss9ERUCLbUJZbilCURUhBlnGo6tQ9ipf8zevQzgwPnQPHDh3jgRsfpLTE+OfsdnsICfepPS54eRHOAJktqqKyae3WinDP2Xi9Xt559UPun3UfAAf3HuShmx+uWBlYLBYWvPwedz9yF5f99tIavUt1qf3nqmlgvkfTIth7iKJQrQlzrRx/cnIysiwzefJkAAYMGEBMTAx2u53s7GxUVUWSJFRVJScnh+Tklqs1oqX0xPGHp5CO7UJwlqLFpqAldq6fDVSLDfelf0YoOI6Uk4FusaG270tYSiJeexJy+iYIIDVdgSTj7T8BAKG8CETZcM/gTMTCzCod/5H0o/y0+CfKSsvoP6w/I8eejyRL/OPPsygpLPHLVKl4JauFkeNGEhXjc/y52XnG5kuS4TiaqrFz8y5emvMysQmxfPXfryutDLxe33u++q//o+/gvnTo0vgZTiYmDU2tHH9sbCzDhw9n9erVjB49moyMDPLz8+nUqROpqaksXryYqVOnsnjxYlJTU1tkfL8SsgW108AGe5wem4ISm1L5WEwy7jHXYft5ESDAmc2vZSvoOp7zf1dRW6BHJYJWtdMH0JK6BD2/4KWFfPruZ6gnNWa+/+oH3oiJ5J5H/0R+br6hsxYlkd4DevOXx++vONaxSwcK8woDXq+qKrJs/NE9fuQ4R9KPIMkSqhK4haaqqiz+aAl3P3Jn0HcyMWmJ1Dqr5/HHH+fvf/87zzzzDLIsM3fuXCIjI5k9ezaPPPIIr776KpGRkTzzzDN1Ya9JNVA7D8LRLhXpyA4ElwPdYkXwesAagtKxH1hDTl+s6+ihUVBa4KsHCIAO6FY70ok0pIwtaG26oXbo68suOsnGVb/y+aLP8ZxRdep0OPG4Pbz23BuIQZrDdOjcnmffrvz5uObWaezZvrdSjB9AtsiMnjCKQwcOcyT9CFqAlc0pZ2/k9E+dO344cCNzE5OWTq0df/v27Xnvvff8jnft2pWPP/64tsOb1BSLvcqGLL5q429A0xDQDdw+IIgIioJl2zIETUVPW4duD8M1+QHflwbw8cJPcJ0lbQ2+mXXm0UzDVERREuk9sLff8UEjBtGzbw+2/7qj0nFJkrjlgVsQBYEZtz5CQW4BXq8XURQrfelUhcVioUvP4CsYE5OWilm52wIQyouQDm7CI3mQwpJRO/SrNBsPhJh1EOvWpQjq6UykU8VeemgkapvuCB4XCCAd21MpVVVQ3FDuxfbT27gmPwBA1vEsw2fZbDZi42M4fuQEmlZ5hi7LMu07t+fNF94iOi6asZddREJCBKt/WkParv1+Y6mKwstPzufJV57gra/eYNuGbRzcl86R9KMs/WxpNX5aJ99fErli2uRqX29i0pIwHX8zR979M9aNX4Cuo2gqNtmGbgvBdfn96OHGeyqWnT8FzPcXAFwOlF4jsf38AUJ5YcAMIUHXEPOPIZTkokcm0L5Te7KOBXb+Ho+H2x68lcfvn+M/jiDw7ivv4XQ4sVgtLHzpHWY8dT+fLvoqYL6/oqhsWbuFwvwiYuKiGTRiEINGDOLLD740/iEBgiggCiIWqwUdncee+zuJyYlB7zExaamYjr8ZI+Ydwbrxy8qzdsUNqgfbD2/guvJh0DWk43sRs9PRraGoXQahh8UgFJwI3L4RQFexrfqfodM/bYCEUFaIHpnAoBED2bhqY8DL7CF23p63MODm7pkxfK/Ht8n83Mx5QatUFUUhY386MXGDTz8jNARBEAw3kDt07sDEKROIjovmgomjCQkNCXidiUlrwHT8zRh513JQ/at4BV339eXN3I99zUe+lE3F7Uvb3LwYz+DLETSlQsPHD11HKCsI7vQBNAU9Ig6ATWs2GV7mKHfgcXv8wjxGeD2KgWGnzNP5x59m8ZfH72f85PEApHRoi9Vm9dsMBl9YZ+ioIfz+5t9V6/kmJi0dU/yjGSMW5Rhm4iCI2NZ9glCSi6C4EQBBUxBUxScfrevBfGuVaqK6IKEldK5w/BlpGYbXVtfhn0JVVfQqCve8Hi8vPv4SR9KPAtBnUB9iE2IRRP+3kmWZK64x4/kmJqcwHX8zRotOQjcqENNUxGKDCl7FA8GyeEQJDGb7OqALInpkPK6xfzw9ZJDUSV3TDUMwgZAkiciYyCqvUxSFr/77FeDbK3j6P08RGx9bEcaxhdiw2qzMeOqvLUK8zcSkrjBDPc0Ypc9Y5EPb/KpudUFAs4cjugJLRfgkmiWQLP73IqCHRqHbwhALjvt/cYginmFXovQe45OKOElVs/q4hFiyT+RUb/YvwNhLx/Ltp98ETBE9haqoHDpwuOLfKR3a8t7Sd1i3ch0Z+w8RExfDmEljiIxqGfosJiZ1hTnjb8Zo8e3xDL/K13tX8n2H67IN3RKC6CgxlGHQAS02Gc+Qyb57BaniOOgIzlLEwhPo1hB02af/rou+Z3iGXYnS56JKTh+oUjL4jhl3EJcYVzEbt1qtWCwWwiPD/TZaNU3j20+/JSw8DEk2TksVRMFPcsFX4DWa6++6jsm/v9x0+iYmATBn/M0cpddo1A79kNI3EyZ5KVctWDctRtCNQy/IVrz9xqMl90BN6Yn925fBVX465n/qC8PtQOk1Gt1qR7eHoXYejB4WHXBIrYqN4IjICN79diFrV65j/679RMVEcdGlFxIZFcn7r3/Ah2/+r6LSVtd0XE4XqqIyYGh/tm7YFnCloGs6I8aOqOpHZGJicham428B6KFRKH3HYk2IwPnJq4Z6/TqAKOPtPxEtuQdy2jqsaz4CTTHY6NWR961Gi2mL69I/gc24GYtYhRid1WZBkiVGjx/F6PGjKp3btXlXQHkFr9fLtl+3I8kSmidALYEg8OuqTZw3OniFsomJSWXMUE8LQyzMNJZYFkSUrkPQLVaktLVY137sy/QxGEvgZKFW4QlsyxcGfW5Ve7cet7EQ3NFDxpo5kiQZhpF0XWf7xu18+u5n/LRkeUAZZxMTE39Mx9/C0CPjMUzU1DXk9M1YN36FbfWH1ZJiBp/zl7IOIJQVGF9Thfq01RpYPx8gKUgFbTChNYDD6Yd568W3mffES0y76BrW/7w+uCEmJiam429pePtcBJJ/BO9UsZagehE0FaGqPP6z0VTkPauQd/+MmHfE77QU4JmnEEWxQgc/EENGDzU8Z7Nbq1TZVLwKTocTl9PFkw8+HVQ3yMTExHT8LQ4tqQueQZeiSxb0k0JtehBJ5HPBsnsl1o1fYP/mJeyLXwSPs+JcsIYmkiwRnxRveH7Xpp2G5xzlTl9bxWqiqipff7i42tebmLRGTMffAlH6T8B51SN4+0/E23MUemjkuc3uA1CxWlAVBMWDmHcY28p3K87/9o+/wR5i97tPlES69+5OmxTjHrcH9h6spXWnUbxKnY5nYtISMR1/C0WPTMA7+DI8o6ahxbU3rNI9VYkL57YyEDQV6cQ+nw4QcN4F59FvSF+/6yRJ4sEnHgg61rnM6KtCFMUGa6RuYtJcMR1/S0DxIh3ahrLtZ8R8/wwZb5+LfG0Xz0JHQI+Ix9t3HN7uI1C6DDWWcQiEKCOU5ACwb2eaX9MU8GXevDP/Xb/jlYapovjrXNA0jVETRlV9oYlJK8bM42/mSEd3YVuxEACPrmPXdbS4drgm3gG2UAC05O54+1yEZedy0FQEXUOXrSBbcV18p6/vLmBd+c65PVxTKzpw/e+tj3C7/eUVFK/C2hXrKMwrJCY+JuAwbdsnB2y6UhNEUWT7hu0MG2W8YWxi0toxHX8zRijOwfbT2whnpGUK+HT6bT+9jfvSP1cc9w6ZjNKxP5at3yE4S9Da98bbZywIoq83r9uBlHmg2nsBOgJaVCJ6VBIAe3fsxWi5oGkax48cr+T4XU4Xv67ZhLPcSe+BvevM8WuaRtruuhnLxKSlYjr+Zoxll28GfzaCpiLlZFR0x4JTK4OTM3pdQyo4jpSxFbEs/+QxPWBHrlP4fLrg680rW9FlK+5xN1ecP9VEJRCqoiLJpz9qPy75iXmPv4Qoiui6FlSI7VwRRZGktmZnLROTYJiOvxkj5h4xbpYiSoiFmaiRCYjH9mD78U2Es74kxMIgXbgCoLbvA7IFtW1PlC5DwGKrOFeVVo96cgN3z/a9vDh7XsCGKXWBxWoxe+mamFSB6fibMXpYNHr+UcMuWro1BNsPryMd2UWgOMw5pXhKFjxDJ6PHBNa1F6oo3ZUsvo/ah298iMdtvLKoDVablWvvmE733t3rZXwTk5aCmdXTjPH2vtAgWwd0exiWvauRju9DMBZxqBY6oIfHGjp9AEk0lk8WRRHV65vxH9h7sNpNWQRBIC4xLqg08ylkWebhf87gmlunVWtsE5PWjOn4mzFa2x6+Ai3JUuHadckCFjvu0dORjmyvtPFbXXROrw902QrWUFzjbw16T9sOQb4UZImYON/GbkxcYFnnCk5+Q9lD7ETHRnPrA7dgs/l/ufndJgr0HpBa5XUmJiZmqKfZ4x1+FWqXwcj71mBTHThjO6H0GIFYcMKn2ROgGfspAjVb1wE9NBq1bQ8Edzlq2x4o3YZXpIYaMXLc+ezeujvgOZvdRkrHFACuvPZKXprzMi7nWUqaArTrmELPfr3wul0MOG8QE64Yj9Vm5bVnX8dR7gww8slbBYF2nVIqsoaOHz5OeloGsfExpA5IrdM6AROTloDp+FsAWkJHPAkdiUqIoDS3FADdHg5B2hz6tPklX7Wu6vW1Y5QsIMm4Jt0ZNKwTiP27DxieczvdlBSVEBkdybjLx7Ju5Xo2/LyhwvnbQmzY7XaefHUObdu3JSEhgtyT7wHwt389zN/ufNSwbaOu62QezWL6+GuJS4rn8IHDyLKMruuERYQxe94sevQx4/4mJqcwHX9LQFOQ963Ds8eJGNcdLbETekwyelgMFGcHmNULKF0G4xn+G8TibOR9qxFcZajJPVB6nl+p4YpQXoSYfxTdGoqW2BkMZs9pO/cZmme1WTmSfoS+g/siiiKPPvs3tqzbwtLPl1FWUsaw0cO4eOpE7CF2tm3chlUWkKwh5OfmEx4exhsvvFnl5rHL6cLldFGQVwhQsYHsdDiZccvDLPzmbaJjqwgzmZi0EkzH38yR96zCuvZjQEcB7IAeEonzqkdwjbuZkCUvoqsKgur1zfJlK0qHfnguvAEEAS0kHE+brv4DK15svyxCOrIDRNmXJSRbcY/9I1qy/+w5KjaazGOB5ZAVRSEqJqri34IgMPj8wQw+f3DFsXUr1zP378+iKioetwdVVRFFsXrN2atAURS++eRbpt/+h1qPZWLSEjCDn80YMTsd69qPKmXtCIDgLCHk83+hxyTj+O1MvH3HosakoLbpjnvczRVOPxi2X95HOrLTp8bpdSEobkRXKfbvX0MoyfW7ftT4kYZjWawW2nc2lm3euGojc/7yJGUlZTgdTlTVV29QF04ffLP/nZuNpZ9NTFobpuNvxljXfRrw+CnnL2YdxLrxCyw7f0Isy0fKP4L1l/cRs4JLGgiOYqTDBhlBquLT/DmLjP2HDMdzOV2UFpcGPP7EA3N47O6ZQSt/a4sgCMTEx9bb+CYmzY0W7/jLy8tZtWod6zdsCtoFqjkiBojfn4l13SfI6ZtPz9q9bkRnKfbvX0cozjYe91RGUAAEXUPMTvc7vn3jdmNDdDh88LDf4adn/IsNv2yodl5/TbHarEz+3WW1H0hVIW0X7NkGLuMsIxOTpk6dOf758+fTs2dP0tLSANi6dStTpkxh0qRJ3HzzzeTn59fVo6qFruu88O9X6Nt/FDff+meuu/52+g0YxVdff9ugdtQneoDirTMRCzONZ+07fjQe1xYatHu6bg/zO+YJoMx5CkVRkOXKXySZx7LYvHZz0Cbs58rZzwDfbH/MxReQWtsc/42rEO6bjvDi4wjzn0K4/1r44v2qu8ybmDRB6sTx79q1i61bt5KS4svV1jSNhx56iJkzZ7Js2TKGDh3Kc889VxePCorD6WTd+l/ZtGkrr72+kPmvvInL5aK0tIyysnKKi0u4/4G/sWZNy2jIrfQcFVw/3+CLwdc83bhLlRbfAd0WEvCcLltRUsf4jxkkV14QBL9mK/t3pQV01DUlPDKczj06I1sqj6nrOmtXrKMkQKip2uzbifDWvxEc5QguFj3jcwAAIABJREFUB4LTgeDxICz9DL7/spaWN0FysmDPdsjLaWxLTOqJWjt+j8fDE088wezZsyuO7dy5E5vNxtChPk30a665hqVLl9b2UYbous78V96gX/+R3HDjnUz7w808MWcuTqf/ctzpdPHscy/Xmy0NiXfQpT4phTOOnaq69fabAEGE03Sb/6y9AkHAPfZmdNlW0bcXfE5fbdcbtWM/v1uSko0VMS1WCxFREZWOnf3vmmK1WQmPDGfG0zM4fPAwite/YM3r9rL005p//oTPFyF4/Fc0gseN8PWHvhBQS6CoAOHphxD+cTfC/CcRHr0TYe7f0IqLGtsykzqm1o5/3rx5TJkyhXbt2lUcy8zMpG3b0wVAsbGxaJpGUVH9fIDefe9DXvj3qzgcTkpLyygvdwSNG2/bvqte7GhwRBHnb2fiHXgJmj0cZCtabDtcl9+Pd+gV6CGBnasuW/GmXhB0aC2xE86r/463z0Woce1RUlJxX3gD7rF/hAAtGqdccwW2EJv/QEBCmwQ6dOlQ6Vj/of2rpcETiMTkRAacN4CefXsw7Zbf89ZXb1JSVGxYoet2u9m6YWuNngXAYePiNLxeKGrYMGa9oKoI/3wI0tMQvB7fqsbrgf17KP7bvUGLAU2aH7Vaa2/ZsoWdO3fy17/+ta7sqURcXHiV12iaxvMvzA84uzciMiKchITKTvHsfzcrJk3z/XcW6lV/wv3hXFC9p2elFhtS+x7EDL8oaHgGgIQI6HxdtUyYdtNUVv+4iq0bduB0+H4XFqsFi9XCs2/MITEx0u+eZ998kvtveNhfvsEAq82C1Wbl1f8+T9deXSqdO37oEJIU+H0EQSCpTXyNf8cFoaHo7sA2CppKbLtExPDAYzeXz5V77c+UlZX49XcQVAUtL4eo42lYBw9rJOvqjuby+6iK2r5HrRz/xo0bOXjwIOPHjwcgKyuLW265heuvv54TJ05UXFdQUIAoikRHn1vlZH5+GZoWfPMsKyub0tKyao9ps9mYNu3qSpIAZ0sENFf83kOOR7jyb8i7VyKd2IduDUXpNQq100BK88vr/PkzX5zFz9/9wpKPvqGstIwh5w/mymunEt8mIeDPt0uvnvzfJ6/y/D+eZ9eW3QFXaXFJcSSnJOModzB8zHlM+cMVRMbF+Y3Xo29vNINVns1uY+zkCTX/HY+aiLDsM4SzssJ0QYBuvcl3Ak7/sZvT50r4dRP/3955x0dVZQ/8+970SU9ooQvSUVAQRHAVcAGlCSKgghVZFwvYAP0puGIBu7ui2BVXwQKiogKu4FJWFAQUEBERIZBQkpA6/b37+2MgEqakJzOZ+/18+HzIK/edM2/umXvPPfccJdTgyekkf8sWaNGxdoWqZqLpfYQjnB6qqpRrwFwlwz958mQmT55c8veAAQNYsGABZ555Jh988AGbN2+mZ8+eLF68mCFDhlTlUSGJi4tD08o3DbXZbLRo0Yzbb7u5RmSJRER8Ct5el1MbgawGg4H+l15M/0svLvc9zVo2Zd5rc5kxaSa/7dpbMvo3moyYzWYemT+Hth3alNGK39c/47HpPD5jLl6vF/3Ed8Jqs9L/sos5q0fXSukEwGVjYOtGxLHDJb5+YTKB2Yq4/vbKtxtBCHscGIwowZL6mcxgC7MmJIk6aiRlg6qqPPHEE8yePRu3202zZs148skna+JRJCTE0+f881i/YWPATk+TyUS3bl0pLnZgNpsYP240Y8eOwm4LHrESdfg8qHlHECZLSYnFaMRkMvHEa/NY/cUaPv/wCzxuN917n8PoCZfTsEn59bpgQB/+teh5PnxrCb/u+JW0hqmMvGYk51/Uu8xcP2Gx2hAPPgMbvoZ1X4HPi+h6LnTuDiZT5duNJM7vD59/CKHWqc/rV6viSGoWRdT07pkqUB5XD8D+/RlcOvRKiosdeDz+5FxWq5VmTZvwxecfkpgY3h8WdVNAoWPa8qW/5q6igq4h4lKwXf43co3R+wNwkoh+H24XytsvwOYNfqPv9UKbDojJ90Bqg1KXRrQewfhsMcrnH4LH7c/WigJmM3ETJ1HU99K6lq7KRN37CEF1uHrqheEHOHYsm9ff+DdffvkVRpOJ8eNGcfVVY4iLK3uK2rBhApmZufz22+9YLBbOOKNVwAjxyJGjHDuWQ6tWLUhIKPuDrUlM336E6ddvAzdnmSw4Rs5AJDYIfmOUUGsd1OmAY4chMRmSy5fSQXnqAfh1J4rvz89eqAZISkHMehbyj0NSCiSlRKeh2fUjysqP4UgmpLdADBlFw759ok+PIETl+wiCNPzVxJKlS/m/B+bi82noukbDhg149ulHueCC3hw6lMVtd0xn69YfMZvNeD1exowZySNzHihXZajqRMk/iuWbt1BzDgZP1aCqeNudj6fv+FqVq7qp8Q7q9aK8uwC+XXOiWM2JUfvNd0NqmBlTxj6UR+8JGtMvVNWf+M5kAZ8X2nchdeYscny1+x2pCWLBYEYT1WH4632unrL497sfMH3GHPLzCyguLsbpdHHgwEEmXPs3vt+8haHDx7Fp0xbcbg+FhUW43G4+WvIpf59yV+0K6irC9tkzoY0+gK5jOBQ6L77Ej7JgHny7xh+v7nL4o3V+3Ynyj2ngCBPt9OuOkCkaFF1H0TR/ez4v/LKd/Hum+F1BEkmEEdOGX9M05s57DocjMIzN5XIzc+ZDFBYWlqQJ/vOcizXfrOP33/+oJUnB9MsG0DxlFk0XpuCbqCQnOHwIdmzxb046BUUIKMxHueta+CbELl+LLWQhmtNRdA29IB9+2FBViSWSaiemDf+hQ1lBjT7400Ds2bM35HlVVfnu+x9qUrxSGDJ2Bg+1OxWjGV+H0HnxJfhH7SEifBROpGFY/CqsXRl4QffeFdvB6nKibK+974hEUl5i2vDbbNaA0fypGEKkJga/4bfZrDUhVlDKGskL1YjSoCm+9n1qSaIoxWwtc9SueNwoSxYG7GIlPgEx9iaE2RKQHyl4QwrYwhepl0jqgpg2/A0bNqBjx+BFuM1mE4MHD8BuD95xfT6NgQMuCjien1/AH38cwO32BLmr8vja9wmZhlmg4OkxFOs194OxnsSV1xRn9ww06MFwuyA7sNIYA4cips2GLucgklMRLc8AQ4icQ2YLos+Aqslb07iccDTLr68kZoj5mrvPPPUoo0Zfg8PpKhn9WyxmGjVqyNzHZjNdm83qNWtLuXxsNitzHv6/UmGdR44c5e57HmDd+o0YjUYU4IYbrmHG9KnVkn5Ya90Nbff/MBzbVxJKKAAMJtx/mYB2xjkoRhMgO3BY7HGI8ZNh8asl8epB0XUwh4jI6Xg2ouPZf/69ahksfadUtI8wW7D0uxhXm/bVJnq14nT4I5s2rQPV4Ne3118QE24BS+3NZCV1g+GhU/MpRxhOp6fG61w0atSA664bw7Fjxzl2NJvU1BRuuH4Czz37OMnJSQwbOpj0Jo05cOAgKAo9e3TniXn/YOhlg0racDgcXDLocnbu/AWfz4fX68Xr9fLT9p3s2bOXYUMHV11QRUVr2wNhiUMtykUoCnp6ezwXXoPezJ9DJS7OgsNRvTONuqDG9Wh9JrTrjPLLdnAWBzf+zVrDpaPL117bjnBGe8jNBq8HmjRDXHEtydfehMMZgVE9QqDMnemP2fd5/RXadA0OZ6D8/CP0+2updRD5vYoswumhKAp2e9khxDKOn6rH977zzvs89PDckAvBXbt24vln59K5c4dKP6M8xEKccrVSVIjyj6mQf/zPWZSqgsmMmDHX/wNRBSL2fez6EeWfc1CCuHeExYq48yFo/2duo4jVo4LEgh4yjr8WWbHy65BGH2DHjl2MvPxq/vjjQC1KJSmT+ATEQ/+EIaMQqQ0QCUl+d8es56ps9CMZZcfW0D59twtlZxVqF0iigpj38VcHVmvZsfNOl4t//utlnnn60VqQSFJu4uIRo6+F0dfWtSS1hjCbUFRD8EVugwFhlntB6jtyxB8GIQS5uccpKgqf73/s2FEho39OomkaX6/+b3WKJ6ksTgcUFcRuofSe/UJHIqkG6CH3gtR3pOEPwRdffkWfvoPofu5f6NTlfC4fdQ2//PJr0GsvGXgRPc7tVmbuHpMMtaxbDv6BMncGyh1Xodx5LcqMSfDD/+paqtqnWSvoOzBgZC/MFvjLYGjSrI4Ek9QW0vAH4bPPVnDrbfeyf38GXq8Xn8/Hd9//wPARV7Fv3/6A6w0GA+/++xVmTp+GIcRIymw2c8UVI6pVTiEEBQWFuFyBScMkp3E0C+Wxe/05eTTNH8mSfQTl1afg+7V1LV2tIyZO8f9r2gJhtSGatURcdxvi6sll3xxTCEAnzDa9qEQa/tPQdZ0HZz+GyxW4+OV0OXn62flB7zOZTNxyy428v+h1bDZrqbTOJpOJBg1SueVvN1SbnEs/Xs55vQfQ9ew+tO/Qg4nX/o0//siotvbrG8oni8AdGLeveDwoi16LvWLiiuIf9T/yEuLFDxFzXoQ+/UOms4hFTLhIIJckckgimzjyUCkjbUqUIA3/aRw8mElBQUHQc5qm8/XX4f30ffuez8dL/03/i/sRF2c/sS/galat/JiUlIrVHA7FO+8s5p57H+DQoSy8Xh9en4/Va9bRu8+lHDuWXS3PqHds34wiQhh3ZzFkH6ldecqDrvvXI2LtRykCMOHCTiEGRUdR/L+HRrzEk4cSskxZ9CCjek7DYDCE3TtgNIZYFDuFbmd35d1/v1qdYpXg8XiY8+hTOJ2lZyS6rlNYWMyrry3k/vtqOWV0NBAuP48Q/kXNSEHTYPn7KF994g+7NJrg4ksRoyf6699KahiBjaKAyY+iAEJgwYGL8FX9Ih054j+Npk2b0KxZetBzJpOREcMvq2WJ/sTt9vDT9p2E2nPn8XhY/nmQrJISOO9CRKhIltQGkFbHJSuF8O/6FQLllSdRvlyC4ij2r0e4XbD6c5SnZ8VuJFItoqKhhPDpKwqYif7dv3LEfxqKovDE3IeYeN0tpUbVBoOBxMRE7ri99he/Vqz8mkcefYp9+/aXWTRcLWe++FhDDBuLsmkdoqjQn56AE8t1Zgvi2lvrzreta/DFEn+5Q2exf3Tv85XIeBLF60Hs/w12b4dT8wRJJJVAWokg9O17Pks+XMiF/fpgNpuJi7Nz5ZiRfLXyYxo3blSrsnzw4TKmTLmbvXv3oes6mqaFTCVttVoYU82RQ/WGpBTE7OfhggH+KBajCTp1Q9z7GHTqVmdiKa89i7L8fZTiQn8VL487dPZQtxtly8baFTAG0TEgQphGIcBD9G9wkyP+EJxzztl88P6bdSqDz+dj9kOP4wwSYXQ6JpORhg3TuOH6a2pBsiglJQ1x41S4cWpdS+InMwN++F9gNbBQ1yvIqJtaQcFBPHGioNTHLYQ/Bbqb6K+xIEf8EczPu3bj84UOHzObzRgMBuLi7IwbO5rvN64gKSmxFiWUVIntmyFUpFEwzBbEef1qTp4yiZ31BR8WikjCJ4x+g39ipF9ISsjZQDQhR/wRTFn+/KZNm/DthlUlf9eX7IMxQ5j3Kyg98hdmM3To6k8BXasINMdxEslDVQS6UHFjw42NMHOTeoGGmSLqZxRV9P901WM6dWyPyRQ8zYPZbObykUNrWSJJtXJWT1BCdEGDERGfgABEQhJi6FjEbQ/UsqtHYKcA3eE3+gCqomOlGBtygBHNyBF/BGM0Gnnk4f/jnnsfLOXnNxgMJCUlMmlS7GSUrJekN4deFyI2rS9dvctkhnadEXfP8R+oI7++AR+mIKGLigJm4cZENgoCgYoLG54YmAXUF+SIP8IZPXo4r7z8HB07tkdRFMxmEyNHXMqqFUtIS02pa/EkVUTcMBUxagIiMdk/urfHIYaM9tf1PblltI4wET4HlKoIFMU/C7BRjI3wWWwlkYMc8UcBl1xyMZdccjGapqGqapm+f0nt4MvMJm/+hzhWb0ZoOtYeHUm+YxyWTq3L34iqwuBRiMGj/GGckbSDOAzBdrWahQs3NnRpViIe+YaiiFCZPyW1j+9wDllXPYBe5CzJpePauIMj236l0UszsHavRJH1CDP6XsxYCF1ZLhgmPLilWYl4pKtHIqkEeS8tQS92BiRQEy4PuY+/XUdSVS8aJnwEBhfIrBHRjzT8EkklcKzeDFrwGHzvvky03OAZXqMLhWKSUGxJCKEgBITJXwj4ZwmSyKdKc7Ljx48zffp0Dhw4gNlsplWrVjz88MOkpqaybds2Zs2ahdvtplmzZjz55JOkpaVVl9wSSd0SzgIqVCqVsi8rm7wFS3Gs/gGEjrXP2aRMuQLTGU0rL2eVUTDGpXHMcXLkL4gnH4PwBexq9WKW/v0ooUojfkVRmDRpEitXruSzzz6jRYsWPPXUU+i6zr333susWbNYuXIlPXv25KmnnqoumSWSOsdybmgfviE5ATUtqULt+TKPkTX+AYo/34AociCKXThXbyJrwmw8uwOrvtU+yol/KkUk48ZasqNVCAUXdhzIXePRQpUMf3JyMr179y75u3v37mRmZrJjxw4sFgs9e/YEYPz48axYsaJqkkokEYRqDZ2oS7GYKxx5dfxfH6AXOUq7j3SBcLjIfeKdyopZQyi4SCCfBhSQRj5puIlDxvBHD9Xm49d1nUWLFjFgwACysrJo2vTP6Wlqaiq6rpOXl1ddj5NI6hTndztDnvMdzkHLq9jOVueaH0K6j9w//obujMS6ysqJvDXS4Ecb1eaQmzNnDna7nQkTJvDVV19VS5tpafHV0k55aNgwuivqnETqUTtk6HrIlGWKqpCaaAXKr8eBEAvF/gahQYodQ0LdZYWM9PdRXiJVDyF0dGc+uqsQhI5iNKPaU1BNtqDXV1WPajH88+bNY//+/SxYsABVVUlPTyczM7PkfG5uLqqqkpxcsZqzOTlFYcsgVhf1JbmZ1KP2sPTsjHPtlqAJK9XUJPIUI42g3HpYzumAa9PPQc8ZWzQm16WBq24+k2h4H+UhcvUQxJOHgT8XzHWPC92ThYMEvFhLXR1OD1VVyjVgrrKr55lnnmHHjh3Mnz8fs9kfytW1a1dcLhebN28GYPHixQwZMqSqj6q3eL1eXnt9IX37DaZL1/MZO+4Gvt24qa7FkoQh+dYxKJYgoYtmEyl3X11hH3/y1HEo1sD2FIuZ1LsjrcaCwISLeHJJJJt4jpeZ3kESGhPuUkYf/szWYaeImkiHXaUR/549e3j55Zdp3bo148ePB6B58+bMnz+fJ554gtmzZ5cK55QEomkaV11zM1u2/IjT6d8luW79t2z+YStPzPsHY64YWccSSoJhSEtEsZoRrtOTmAlMzStepc3SpQ2NXpxO7mNv4d1/GBQwNkolZfpEbH0jq9SijSLMuEoMlYoPgyjAhf3EIq+kIpz6WZ6OAIx48VXz/ghFhKrcHQHEgqtn+ecrmTrtPhwOR8C5uDg723/8HzabNcidgUTuVLZiRIMeOXMXUrTka/AF+ubNZ7UlfeFDldZDyy0AXUdNS4qIvEyn6mHARzzHgxoqIaCAVASRk3pCxYcFByY8qAYDTs18opZA5Oxdjec4RiV4wSVdKDhIwHdKuceIcPVIqsb77y8NavQBVEVlwwZZYzUSKf58Q1CjD+DZ9QdafuUzVRpSEzE0SI4Io386JsKXAY0kl48BLwkcx4zbX09A92HFQQLHUaj4Bruawos5ZBoMBYEWJG1GVYnZbXZCCJZ+vJxXXn2LY8eyadeuLVPvuIUL+vSqVTmKHaGTYAkETmfZ9XYltY8I9140HeH2VstztOOFFLzzJcUrvwVdYB/Qk8TrhmJsVH0puRV0LDgw40YAXiy4sYcoMSjCZoqOpJ8qG4VBs4iqwq+vi9qLGgyHB5s/GZ4QAbuh3dhqpNRjTBp+IQS3T53Bl198heOEXz0r6wibNm3hodkzuXbi+FqTZdBfL2bbtp+CGniv10uvXufWmiySChDO+gmBYqn6KM13LM+fAbSgGLx+V0DhB/+hePl6mrz7cKXWEk5HQTsxAv7T6KjCiRnXifqypd02PswI4UZRgg9RgyV1qwsUNAxowc+dKCQTKYZfoFJECjYKMIqTLh8FF7YaK+wec66eg4cymTT5DpYu/azE6J/E6XQxa/bj5OdXPMGWrussWfoZQy4bQ4/z+nP9jbeydetPYe/Zt28/O3f+gtsdWOXIZrNyxegRNG5c9c4tqX4UQ5iuYzRUy4g/74UP0PMKS4w+AD4NvchB7ryFIe9z/u8njkx+jINDpnJ40qM4120Lea2NolJGH05ElCCwURxwvQ8zOmqAa0II0DCiRchYUikzEiayljZ1DBSTQgFpFJJS47uhI+Mt1RK7d+9h+MirKCoqJtSattFo4Kv/fMOYK0aUu10hBLf8/S6+Xv1fHI6TM4jDrF27gaeffIRRo4YF3LN160+MGXs9brcb/ZSEXqqqYrGYuf66q7n/vrsqqKGktrCc0wHXxh1BzxlSEjA0qFiunmA4Vn0XPAOoLnBt3IHweFHMpUfYea8so+DNz0qijbQjuRzbuY+ECYNJufXK0xoSmPAEnbwoCpiEm0ADqVBEMjYKMQkPfsMk8GDBSQKR4uzRMSBQgv4ACBE5M5PTEag14to5nZga8d9z74NhjT6ApukUFwdfbA3F2nX/4+vVa0uMPvh/DJxOF/dMfzBgZgFw+9QZOBwONK30dNRoNHLntL8z68HpGI0x9bscVSTfdmXwuHurmeQ7xqGoVetars27goSKnoIQCG/pSBBfZjb5r38acJ9wuSl850u8GUeqJFNJe6g4SDoxOk0mnzScJBIpRt+Pgou4kIumrhgPO40Zw5+be5yftu8Ma/QBfD4v5/fuUaG2Fy1aEjoyR1X55pv1pY7t27efzMysoNd7PB7e/2BZhZ4vqX0sXdrQ8Lm7MDRtiGI1o9gtqEnxpNw7kfhh/arUdsG7Kzh6e/hstoZGKahxpbfzF3/1XcgqKULTKV55eoSYgh4m9FLDQDhjLlBPpGGOTDPiwYaTOHShIIQ/u6gmVIpIivn00TGjvcPpPFG6MLzvVdcFTZumV6jtgoLQawJCCIqKSvtKi4qLMRhCf/TFxYG+VUnk4N6xl/y3Pse75wDGpg2w3zAMS/d2mFs3RTFWLYZdy87j+D8/AE/476maEpgCWS92lV4POBWfhigKHJw4iSNOFAS4e4QgYhY/q4IHOx5sqGikpsSTl+sksmYmdUNk/lTXAOlNGmOzBU94dCo2m5VVX62pUNsXX3xhyE1WPp9Gr/NKR+a0O7MtIkShDoNBpW/f8yv0fEntUfTpOo7c/BjO1ZvxHTiCe/Mu8p55j+Ila6ps9AGK/7MpbMDQSby/Hghw9VjPbY9iD/49VOxWrD06BRz3b29KKBkV+6tsndw0VF+qaSnoGFEMJqTR9xMzht9gMDD93jvKNP6apgeM0Mti/LjR2Gw21NP8ularhQH9L6R165YBx6dMuSmoLGazhTtu/1uFni+pOJ5fD5D9fy9xaPQMjtwyD8e6bWW6AfVCB7mPven3oZ9yrXC6KVr2X9w7f6+yXKLYifAGD0MsfSGI02YF1l5dMDZrCKbTfoCMBgyNU7FeEDz1gxdrib++8ERkyemJwST1i5gx/ADXThzPzBnTsJhDj2QUBc7reU6F2k1MTODzz97nrK6dsVotJCYkYLFYGDniMl6c/3TQe+6cNoVbp9yE3W4jISEeu91GyxbNWfTea7Rv17ZCz5dUjOL/fM/h6/5B8YqN+PZl4vpuB9kzXiD38bfC3uf4ZguECOMUHi9Fn62rsmyWbu3KtQfA0CglYHSvqCqNX70fa++uYDahxNvAbMJ6XmeavP5A+BDUE6Niv+9bjorrOzHj4wd/qcjJN1/HlWNG0rffYPILCkuFUlosFnqc253OnTtUuO3WrVuy4suPOHDgINnZObRp05rk5NAhfYqicPddt/H3v9/E7l/2YI+z075d24jcpl+f0J1ucma9Ehj54nRT/NkG4of2w9yxFUWfb6Bo2VqEx4O9f08SrhyIXuRAhEjTgC7Qq5Cm4SSWHh0xtUrH81sG+EJsQLKaSb5jbNDviiEpnsb/ugctOw9fZjaGJmnVustXUj+IKcN/kpSUZFauWMLkv93JL7t/xWKx4HK5GPTX/jz7zKNVartly+a0bNm83NfbbTbOOSeysi/WZ5zrt4XcdSvcHgo++hrvngx8+48gXP68M/n7sihctIrU2ZNQ1OBbgxSbBWuvLlWWT1EU7MP74Xn63cCTJiOKxUTK1PHED+kTth1Dg2QMDSpW/0ISO8Sk4Qdo0aI5X37xIfv3Z+D2FJOa0pAGDdLqWixJDaMXOiDEwjpC4Nm+F19WTumoGo8X3eej8N0VmDu2wr1zX+noGVVBsVuJG1L1RXktt4C8f74fogyjoMlbszG3bVbl50him5jy8QejVasW9L3gPGn0YwTLWWciQpU5NKhoOfnBQyl1gfun30h75BZsfc4q8aErFjOm9q1o8tYs1HKmzw6H4z/fh3b3CXCsktlaJVUnZkf8ktjE2KJx8DQI4D8ewq8OlIRrNnr+LnxHj+M7cBhDwxRMrZpUm3x6oQPhCR2Lrx2P7DoF1YkBLxYcqOhoGHFji/mNV9WF/BQlMYVz/Y/+ylmOIGmVVQUl3hY6VYLRgLFRqv+/jVJqZNHU3KVNSPkUuxXrORUPPIhGLBRjxb/hTFHAIHyYcQWtQSupODHv6pHEFnpBUWgf/0m/ehBXi2KzkHTDMBRTzY6VrL06Y2ySBqdvBlMV1Hgb9oE9a/T5kYCKv2DKybqzcGoN2kIiLbNmNCINvySmsHRpE/a8nltQOt/NiUiahKsHkXh9YJbV6sYfi38flrPaolhOrCNYzZjataTJmw8GZOOsj5ijqMpXtFIvXT0/bd/JG2++y4EDBzmrayduvGECefn5vPnWexw4cJBuZ3fhhuuvqVDYpaR+YO5E9xtgAAAN60lEQVTQCnOHVrh/3hc8r02QaJpGL82oVReLITWJJm88iHf/YXwZRzCkN4ipSB4VrYwqX3LEX1XqXbH1+S++ztPP/Au324Ou65hMJoTQUVUDPp+v5JjRaOTVV55n4IC/REVx7/Ig9SgfemExx+59AdfW3SgmI8LrBa8WPLOlqhI/ZgBp911X4efI91E5zDiwUowaoqB7EcmVqkMbC++jvMXW65Xh3/Pb7wwaPAqXq3xTwbg4O9t//B8tWzYs1xeiuLiYj5Z8ysqVX2O32xk3dhQDB14UkKOnroiFL3Z14s04gvf3Q7h/3EPhe6sQQSqhARhaNsGUnoaxWSMSxg7E3KFVudpPdDvZ/9IneH75A2OLxiSMvQRzuxbVqUKtUPvfK51EcgMqg52s8lVEMpVJKxEL/aO8hr9euXoWLfoIX5hwvNNRFIWvvlrDTTeNLfPazMzDXDZsLIWFhSUFV775Zj3nnXcu7yxcIIumRCGmFo0xtWiMIS2JgndXhrxOO3QU7cBhUHdR/MUGkm4ZTdJ1Q8O27Vy3jYwZL6D7fP7ZxOZdFC9fT/Id40i8alB1q1LPUCkimTjyUcWfC/EaRopJQuYSqjqRMVStJrKyjuDzhYiBDoLX6yM7J6dc10678z6ys3NKVdkqdjj47vvNLFy4OOR9P23fyb3TZ3HNxMk8+9yLZGf/+byioiLeevs9rr3uFm697V7Wrfu2zAyRkurH1K4FhPvenIz713WEy0P+S0vw/n4o5OW6w8WxGS+gO91+o3+iDeHykPf84mqrhFWf0TFSSCpFJOMgkUJSKCKlVsoSxgL16lPs2bN7uXLun8RgUOnSJTBH+enk5B7nu+9/CCiTCP4C7a+/8U7Q+556+gUuH3UN7y36iNWr1/L8P1+mT99B/PDDNjIOHuKCfkOY88iTfPWfb1j68Wdcf+MUJt8yrVTiOEnN49rwEwQpoxgK4dMoXBq6ZoNjzQ+h8wFpOkWfrK2wjLGJgoYJLxa5cauaqVeG/8oxl2MylX/Rx2g04ih2lGloc3OPh203Nzcv4Ni2bdt58aXXcDpdJe273W6Kioq5/sZbufXWe8jJyS01g3A4nKxevZaPlnxabh0kVUfLyUfRKjDT0nS0w7khT+s5+f4F42D4NLQj5ZtlSiQ1Rb0y/ImJCSz9aCHp6Y2Ji4sjPi4Om81G27ZtaNSwAXa7vVQelIKCQiZNnsrwERPDuoiaNUsP++PQsVP7gGNvLVyEO8RiocPhYNuPO4K26XA4efW1t8OpKalmTO1aEDSEJASK1Yz57DNDt9e+JUqIgYJis2A+K/S9EkltUK8MP0CXLh3Z/P0aFr79EvPmPcQny95l/dov2LplLWd17RQQgeNwOFi37jtef+PfIdu022xMnDAuaHlFm83KXdOmBBw/ePBQyB8LXRcn6v8G59gxOSKsTSzd2mFs1ihkkZUADAbiR/4l5Glrr84Y0pKCtqcYDcRf1reyokok1UK9M/wAqqpyQZ9ejB41nLO6dgb8Bn7rtu1B/fQOpzOkn/4kD/zf3Qy9bDAWi4W4ODvx8XFYrVYefug+LrwwMDf6ued0wxyy0pcIO4OoTCEYSeVRFIXGL03H3K4FitWCYregxFlRkuMxtmqCYjtxzG5FTUuk8cszMSSFDpkrqYR1ZvNS9xoaJtP41ftR48u/DiWR1AT1Ko4/HBkZB7l4wPBSPvVTibPb+W3PljLbOXgok40bN2O1Wuh/cT/i4uKCXpeZeZgLL7o04Hkmk5GuXTvTsUM7Pl62PGDPgc1mZdF7r9O7V49yavYnsRCnXNO4f96Hd+8hDA2TsJ7XBcWg4t75O97fMzE0TMZ6XueSEoa6rlO0aBWFS1aDV8M+sCdJt1yBemKhuEGDeA6t2Yb3j8MYGqdg7dkJJUL2fFQE+b2KLGQcfwVo1KgRSpj437ZtzyhXO82bNWXMFSPKvK5p0ya88/YCbpx0O5qmoZ0ICWzfri0L315AfFwcx4/n880361ANKqqqovk0HnvkwUoZfUn1YOl8BpbOpb8Lli5tAnL86C4Ph4bdiZ5TUHKs4O0vKHhvFc2WPYGxaUMURcFy1plYpE9fEmHEjOG3WMxce+143nr7PZzO0kmg7HYbd94Z6KevKhdc0Juftq3nv//dQHZOLp07d6Db2V1Lzr/5xgv8/vsffL/pB+LscQwYcGHIGYQkssi+b34po1+C18fhSY/S/Ivnal8oiaSc1Kjh37dvHzNnziQvL4/k5GTmzZtH69ata/KRYblv5p0cPnyEL778D6qqoqoKPp/Ggw/cyZDBA2vkmWazmb/+tX/I823atKZNm9Y18mxJzeFcuy3kOS0rB9/xAmiYUIsSSSTlp0YN/+zZs7n66qsZOXIkn3zyCbNmzWLhwoU1+ciwmEwmXpz/NPv3Z7B+w0bMZjMDB/yFDh1a1gvfn6QWKWPvh3YoG9rHTkZNSXRRY4Y/JyeHn3/+mTfffBOAYcOGMWfOHHJzc0lNTa2px5aLVq1a0KpV9CXLkkQQJmPwtM4nT7dpWovCSCQVo8YMf1ZWFo0bNy6JVzcYDDRq1IisrKxyG/7yrE5XFw3rybRc6lE7OCcO4egby4Oes5/dlsatGgKRr0d5kXpEFlXVI6IXd6sznDMcsRDmFU1Egx6WW6/EvPVXPFt/LXVcbZhC2oKZHDtWGBV6lAepR2QR0eGc6enpHDlyBE3TMBgMaJrG0aNHSU9Pr6lHSiS1hqqqpL/xIO5f9lPwzhcIj4/4ERdiv7B7XYsmkZRJjRn+tLQ0OnXqxPLlyxk5ciTLly+nU6dOde7fl0iqE0vHVjR89O91LYZEUiFq1NXz0EMPMXPmTF588UUSExOZN29eTT5OIpFIJOWgRg1/27Zt+fDDD2vyERKJRCKpINGXOEQikUgkVSKio3rUCuRIj6Zn1SRSj8hC6hFZ1Hc9yqtfRGfnlEgkEkn1I109EolEEmNIwy+RSCQxhjT8EolEEmNIwy+RSCQxhjT8EolEEmNIwy+RSCQxhjT8EolEEmNIwy+RSCQxhjT8EolEEmPElOE/fvw4N998M4MHD2b48OHcdttt5ObmArBt2zZGjBjB4MGDufHGG8nJyaljacvHCy+8QIcOHfj1V39BkGjTw+12M3v2bAYNGsTw4cN58MEHAdi3bx/jxo1j8ODBjBs3jj/++KNuBS2DNWvWcPnllzNy5EhGjBjBqlWrgMjXY968eQwYMKDUdwjCyx2JOgXTI1x/h8jsK6Hex0lO7+9QST1EDHH8+HGxcePGkr/nzp0r7rvvPqFpmrjkkkvEpk2bhBBCzJ8/X8ycObOuxCw3O3bsEDfddJPo37+/2L17d1TqMWfOHPHoo48KXdeFEEIcO3ZMCCHExIkTxbJly4QQQixbtkxMnDixzmQsC13XRc+ePcXu3buFEELs2rVLdO/eXWiaFvF6bNq0SWRmZpZ8h04STu5I1CmYHqH6uxAiYvtKqPchRGB/F6LyesSU4T+dFStWiOuuu078+OOPYujQoSXHc3JyRPfu3etQsrJxu91i7NixIiMjo+SLEG16FBUViR49eoiioqJSx7Ozs0WPHj2Ez+cTQgjh8/lEjx49RE5OTl2IWSa6rotevXqJzZs3CyGE+P7778WgQYOiSo9TjUk4uSNdp2AG8yQn+7sQIuL7yul6BOvvQlRej4jOzlmT6LrOokWLGDBgAFlZWTRt2rTkXGpqKrquk5eXR3Jych1KGZrnn3+eESNG0Lx585Jj0aZHRkYGycnJvPDCC3z33XfExcUxdepUrFYrjRs3xmAwAGAwGGjUqBFZWVkRWcFNURSee+45pkyZgt1up7i4mFdeeYWsrKyo0uMk4eQWQkSlTqf2d4i+vhKsv0Pl9YgpH/+pzJkzB7vdzoQJE+palAqzdetWduzYwdVXX13XolQJTdPIyMigc+fOLF26lHvuuYfbb78dh8NR16JVCJ/Px8svv8yLL77ImjVreOmll5g2bVrU6VGfkf29NDE54p83bx779+9nwYIF/qLZ6elkZmaWnM/NzUVV1Yj85QfYtGkTe/fuZeDAgQAcPnyYm266iYkTJ0aVHunp6RiNRoYNGwZAt27dSElJwWq1cuTIETRNw2AwoGkaR48eJT09vY4lDs6uXbs4evQoPXr0AKBHjx7YbDYsFktU6XGS9PT0kHILIaJOp9P7OxBVfT5Uf3/88ccrrUfMjfifeeYZduzYwfz58zGbzQB07doVl8vF5s2bAVi8eDFDhgypSzHDMnnyZNavX8/q1atZvXo1TZo04fXXX2fSpElRpUdqaiq9e/dmw4YNgD9aJCcnh9atW9OpUyeWL18OwPLly+nUqVPEuhKaNGnC4cOH+f333wHYu3cvOTk5tGrVKqr0OElaWlpIucOdi0SC9XeIrj4fqr/369ev0nrEVCGWPXv2MGzYMFq3bo3VagWgefPmzJ8/ny1btjB79mzcbjfNmjXjySefpEGDBnUscfkYMGAACxYsoH379lGnR0ZGBvfffz95eXkYjUamTZvGRRddxN69e5k5cyYFBQUkJiYyb9482rRpU9fihuTTTz/l1VdfRVH8FZDuuOMOLrnkkojX45FHHmHVqlVkZ2eTkpJCcnIyn3/+eVi5I1GnYHo899xzIfs7EJF9JdT7OJVT+ztUTo+YMvwSiUQiiUFXj0QikcQ60vBLJBJJjCENv0QikcQY0vBLJBJJjCENv0QikcQY0vBLJBJJjCENv0QikcQY0vBLJBJJjPH/V6n7vcD0EFkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mKBYBdKjSrC2"
      },
      "source": [
        "## Problem 2: Clustering (Manually)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Enllke2tSrj6"
      },
      "source": [
        "For the following dataset, perform the clustering “by hand”:\n",
        "\n",
        "17 28 50 60 80 89 150 167 171 189 \n",
        "1. \tUse the K-means algorithm with K= 3 to cluster the data\n",
        "2. \tUse hierarchical agglomerative clustering with single linkage to cluster the data\n",
        "3. \tUse hierarchical agglomerative clustering with complete linkage to cluster the data\n",
        "4. \tFor K-means What will the final clusters be after 3 iterations if k=3 and the initial centers are 150, 171 and 189"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "dlJSeilW_mpi",
        "outputId": "6291ec02-f839-4cf1-8499-36ad869192ea"
      },
      "source": [
        "#1. \tUse the K-means algorithm with K= 3 to cluster the data\n",
        "'''\n",
        "a) initial partition: 17, 80, 167. Assign other numbers to the closest center\n",
        "cluster 1: 17,28,50, mean = 31.7\n",
        "cluster 2: 60,80,89, mean = 76.3\n",
        "cluster 3: 150, 167, 171, 189, mean = 169.25\n",
        "b) reevaluate each number to the clusters mean\n",
        "c) each number is assigned with its clostest cluster. the final assignment is:\n",
        "cluster 1: [17,28,50]\n",
        "cluster 2: [60,80,89]\n",
        "cluster 3: [150, 167, 171, 189]\n",
        "'''"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\na) initial partition: 17, 80, 167. Assign other numbers to the closest center\\ncluster 1: 17,28,50, mean = 31.7\\ncluster 2: 60,80,89, mean = 76.3\\ncluster 3: 150, 167, 171, 189, mean = 169.25\\nb) reevaluate each number to the clusters mean\\nc) each number is assigned with its clostest cluster. the final assignment is:\\ncluster 1: [17,28,50]\\ncluster 2: [60,80,89]\\ncluster 3: [150, 167, 171, 189]\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "jroYcwLclqUM",
        "outputId": "cf4f61e6-2b41-4588-96e6-d0bfc48d2d24"
      },
      "source": [
        "#2. Use hierarchical agglomerative clustering with single linkage to cluster the data\n",
        "'''\n",
        "a) find closet distance for each number and its neighbor. \n",
        "17 (11) 28 (22) 50(10) 60 (20) 80 (9) 89 (61) 150  (17) 167 (4) 171 (17) 189 \n",
        "\n",
        "b) minimum value is 4, between 167 and 171. the updated distance for the numbers are:\n",
        "17 (11)28 (22) 50(10) 60 (20) 80 (9) 89 (61) 150  (17) [167,171] (17) 189\n",
        "  clusters are: 17 28 50 60 80 89 150 [167, 171] 189 \n",
        "\n",
        "c) minimum value is 9. update the distance list with 8 clusters:\n",
        "17 (11)28 (22) 50(10) 60 (20) [80,89] (61) 150  (17) [167,171] (17) 189\n",
        "  clusters are: 17 28 50 60 [80, 89] 150 [167, 171] 189 \n",
        "\n",
        "d) minimum value is 10. update the distance list with 7 clusters:\n",
        "17 (11) 28 (22) [50,60] (20) [80,89] (61) 150  (17) [167,171] (17) 189\n",
        "  clusters are: 17 28 [50,60] [80, 89] 150 [167, 171] 189 \n",
        "\n",
        "e) minimum value is 11. update the distance list with 6 clusters:\n",
        "[17,28] (22) [50,60] (20) [80,89] (61) 150  (17) [167,171] (17) 189\n",
        "  clusters are: [17,28] [50,60] [80, 89] 150 [167, 171] 189 \n",
        "\n",
        "f) minimum value is 17. update the distance list with 5 clusters:\n",
        "[17,28] (22) [50,60] (20) [80,89] (61) [150,167,171] (17) 189\n",
        "  clusters are: [17,28] [50,60] [80, 89] [150,167,171] 189 \n",
        "\n",
        "g) minimum value is 17. update the distance list with 4 clusters: \n",
        "[17,28] (22) [50,60] (20) [80,89] (61) [150,167,171,189]\n",
        "  clusters are: [17,28] [50,60] [80, 89] [150,167,171,189]\n",
        "\n",
        "h) minimum value is 20. update the distance list with 3 clusters:\n",
        "[17,28] (22) [50,60, 80,89] (61) [150,167,171,189]\n",
        "  clusters are: [17,28] [50,60, 80,89] [150,167,171,189]\n",
        "\n",
        "i) minimum value is 22. update the distance list with 2 clusters:\n",
        "[17,28,50,60, 80,89] (61) [150,167,171,189]\n",
        "  clusters are: [17,28,50,60, 80,89] [150,167,171,189]\n",
        "\n",
        "'''"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\na) find closet distance for each number and its neighbor. \\n17 (11) 28 (22) 50(10) 60 (20) 80 (9) 89 (61) 150  (17) 167 (4) 171 (17) 189 \\n\\nb) minimum value is 4, between 167 and 171. the updated distance for the numbers are:\\n17 (11)28 (22) 50(10) 60 (20) 80 (9) 89 (61) 150  (17) [167,171] (17) 189\\n  clusters are: 17 28 50 60 80 89 150 [167, 171] 189 \\n\\nc) minimum value is 9. update the distance list with 8 clusters:\\n17 (11)28 (22) 50(10) 60 (20) [80,89] (61) 150  (17) [167,171] (17) 189\\n  clusters are: 17 28 50 60 [80, 89] 150 [167, 171] 189 \\n\\nd) minimum value is 10. update the distance list with 7 clusters:\\n17 (11) 28 (22) [50,60] (20) [80,89] (61) 150  (17) [167,171] (17) 189\\n  clusters are: 17 28 [50,60] [80, 89] 150 [167, 171] 189 \\n\\ne) minimum value is 11. update the distance list with 6 clusters:\\n[17,28] (22) [50,60] (20) [80,89] (61) 150  (17) [167,171] (17) 189\\n  clusters are: [17,28] [50,60] [80, 89] 150 [167, 171] 189 \\n\\nf) minimum value is 17. update the distance list with 5 clusters:\\n[17,28] (22) [50,60] (20) [80,89] (61) [150,167,171] (17) 189\\n  clusters are: [17,28] [50,60] [80, 89] [150,167,171] 189 \\n\\ng) minimum value is 17. update the distance list with 4 clusters: \\n[17,28] (22) [50,60] (20) [80,89] (61) [150,167,171,189]\\n  clusters are: [17,28] [50,60] [80, 89] [150,167,171,189]\\n\\nh) minimum value is 20. update the distance list with 3 clusters:\\n[17,28] (22) [50,60, 80,89] (61) [150,167,171,189]\\n  clusters are: [17,28] [50,60, 80,89] [150,167,171,189]\\n\\ni) minimum value is 22. update the distance list with 2 clusters:\\n[17,28,50,60, 80,89] (61) [150,167,171,189]\\n  clusters are: [17,28,50,60, 80,89] [150,167,171,189]\\n\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "w2gJgmotk3Em",
        "outputId": "0711eea0-63dd-49be-f0ff-2c4b72b6f855"
      },
      "source": [
        "#2. Use hierarchical agglomerative clustering with complete linkage to cluster the data\n",
        "'''\n",
        "a) find closet distance for each number and its neighbor. \n",
        "17 (11) 28 (22) 50(10) 60 (20) 80 (9) 89 (61) 150  (17) 167 (4) 171 (17) 189 \n",
        "\n",
        "b) minimum value is 4, between 167 and 171. the updated distance for the numbers are:\n",
        "17 (11) 28 (22) 50(10) 60 (20) 80 (9) 89 (61) 150  (21) [167,171] (21) 189\n",
        "  clusters are: 17 28 50 60 80 89 150 [167, 171] 189 \n",
        "\n",
        "c) minimum value is 9. update the distance list with 8 clusters:\n",
        "17 (11)28 (22) 50(10) 60 (29) [80,89] (70) 150  (21) [167,171] (21) 189\n",
        "  clusters are: 17 28 50 60 [80, 89] 150 [167, 171] 189 \n",
        "\n",
        "d) minimum value is 10. update the distance list with 7 clusters:\n",
        "17 (11) 28 (32) [50,60] (39) [80,89] (70) 150  (21) [167,171] (21) 189\n",
        "  clusters are: 17 28 [50,60] [80, 89] 150 [167, 171] 189 \n",
        "\n",
        "e) minimum value is 11. update the distance list with 6 clusters:\n",
        "[17,28] (43) [50,60] (39) [80,89] (70) 150  (21) [167,171] (21) 189\n",
        "  clusters are: [17,28] [50,60] [80, 89] 150 [167, 171] 189 \n",
        "\n",
        "f) minimum value is 21. update the distance list with 5 clusters:\n",
        "[17,28] (43) [50,60] (39) [80,89] (91) [150,167,171] (39) 189\n",
        "  clusters are: [17,28] [50,60] [80, 89] [150,167,171] 189 \n",
        "\n",
        "g) minimum value is 39. update the distance list with 4 clusters: \n",
        "[17,28] (43) [50,60] (39) [80,89] (109) [150,167,171,189]\n",
        "  clusters are: [17,28] [50,60] [80, 89] [150,167,171,189]\n",
        "\n",
        "h) minimum value is 39. update the distance list with 3 clusters:\n",
        "[17,28] (72) [50,60,80,89] (139) [150,167,171,189]\n",
        "  clusters are: [17,28] [50,60, 80,89] [150,167,171,189]\n",
        "\n",
        "i) minimum value is 72. update the distance list with 2 clusters:\n",
        "[17,28,50,60,80,89] (172) [150,167,171,189]\n",
        "  clusters are: [17,28,50,60, 80,89] [150,167,171,189]\n",
        "'''"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\na) find closet distance for each number and its neighbor. \\n17 (11) 28 (22) 50(10) 60 (20) 80 (9) 89 (61) 150  (17) 167 (4) 171 (17) 189 \\n\\nb) minimum value is 4, between 167 and 171. the updated distance for the numbers are:\\n17 (11) 28 (22) 50(10) 60 (20) 80 (9) 89 (61) 150  (21) [167,171] (21) 189\\n  clusters are: 17 28 50 60 80 89 150 [167, 171] 189 \\n\\nc) minimum value is 9. update the distance list with 8 clusters:\\n17 (11)28 (22) 50(10) 60 (29) [80,89] (70) 150  (21) [167,171] (21) 189\\n  clusters are: 17 28 50 60 [80, 89] 150 [167, 171] 189 \\n\\nd) minimum value is 10. update the distance list with 7 clusters:\\n17 (11) 28 (32) [50,60] (39) [80,89] (70) 150  (21) [167,171] (21) 189\\n  clusters are: 17 28 [50,60] [80, 89] 150 [167, 171] 189 \\n\\ne) minimum value is 11. update the distance list with 6 clusters:\\n[17,28] (43) [50,60] (39) [80,89] (70) 150  (21) [167,171] (21) 189\\n  clusters are: [17,28] [50,60] [80, 89] 150 [167, 171] 189 \\n\\nf) minimum value is 21. update the distance list with 5 clusters:\\n[17,28] (43) [50,60] (39) [80,89] (91) [150,167,171] (39) 189\\n  clusters are: [17,28] [50,60] [80, 89] [150,167,171] 189 \\n\\ng) minimum value is 39. update the distance list with 4 clusters: \\n[17,28] (43) [50,60] (39) [80,89] (109) [150,167,171,189]\\n  clusters are: [17,28] [50,60] [80, 89] [150,167,171,189]\\n\\nh) minimum value is 39. update the distance list with 3 clusters:\\n[17,28] (72) [50,60,80,89] (139) [150,167,171,189]\\n  clusters are: [17,28] [50,60, 80,89] [150,167,171,189]\\n\\ni) minimum value is 72. update the distance list with 2 clusters:\\n[17,28,50,60,80,89] (172) [150,167,171,189]\\n  clusters are: [17,28,50,60, 80,89] [150,167,171,189]\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "L7b2NNqJlTl8",
        "outputId": "e36b4dcf-f60e-4c7c-a380-3f5780512e20"
      },
      "source": [
        "#4. \tFor K-means What will the final clusters be after 3 iterations if k=3 and the initial centers are 150, 171 and 189\n",
        "'''\n",
        "17 28 50 60 80 89 150 167 171 189 \n",
        "a) initial partition: 150, 171, 189. Assign other numbers to the closest center\n",
        "cluster 1: 17 28 50 60 80 89 150, mean = 67\n",
        "cluster 2: 167,171, mean = 169\n",
        "cluster 3: 189, mean = 189\n",
        "\n",
        "b) reevaluate each number to the clusters mean. Assign numbers to the closest center. calculate new means\n",
        "cluster 1: mean = 67, assign 17 28 50 60 80 89, new mean = 54\n",
        "cluster 2: mean = 169, assign 150 167 171, new mean = 162.7\n",
        "cluster 3: mean = 189, assign 189, new mean = 189\n",
        "\n",
        "c) each number is assigned with its clostest cluster. the final assignment is:\n",
        "cluster 1: [17,28,50,60,80,89]\n",
        "cluster 2: [150, 167, 171]\n",
        "cluster 3: [189]\n",
        "\n",
        "'''"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\n17 28 50 60 80 89 150 167 171 189 \\na) initial partition: 150, 171, 189. Assign other numbers to the closest center\\ncluster 1: 17 28 50 60 80 89 150, mean = 67\\ncluster 2: 167,171, mean = 169\\ncluster 3: 189, mean = 189\\n\\nb) reevaluate each number to the clusters mean. Assign numbers to the closest center. calculate new means\\ncluster 1: mean = 67, assign 17 28 50 60 80 89, new mean = 54\\ncluster 2: mean = 169, assign 150 167 171, new mean = 162.7\\ncluster 3: mean = 189, assign 189, new mean = 189\\n\\nc) each number is assigned with its clostest cluster. the final assignment is:\\ncluster 1: [17,28,50,60,80,89]\\ncluster 2: [150, 167, 171]\\ncluster 3: [189]\\n\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TuA0ZcPfRu0e"
      },
      "source": [
        "## Bonus points\n",
        "Use the dataset of accepted papers at the AAAI 2014 conference to find clusters of papers using K-Means. You can use paper title or abstract to build your features using [Bag of Words](https://en.wikipedia.org/wiki/Bag-of-words_model).\n",
        "\n",
        "1. Vary the number of K from 2 to 6 and show if the results vary and assess the clusters obtained.\n",
        "\n",
        "2. Make a case regarding which clusters ‘make sense’ e.g., is there a cluster were papers on reinforcement learning are together vs. another cluster which has papers on deep learning."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgqqIL0PRu0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "6b48ea09-c8b4-4c9d-80ab-b3b2eb367f18"
      },
      "source": [
        "# Load the Relevant libraries\n",
        "import sklearn as sk\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import nltk\n",
        "import re\n",
        "import os\n",
        "import codecs\n",
        "from sklearn import feature_extraction\n",
        "import mpld3\n",
        "from textblob import TextBlob\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "# URL for the AAAI (UW Repository)\n",
        "aaai_data = pd.read_csv(\"https://raw.githubusercontent.com/zariable/data/master/AAAI2014AcceptedPapers.csv\")\n",
        "\n",
        "aaai_data_clustering = aaai_data.iloc[:,5]\n",
        "display(aaai_data_clustering.head())\n",
        "\n",
        "# TODO: retain only anual_income and spending_score for clustering\n",
        "#shopping_data_clustering = shopping_data.iloc[:,3:]\n",
        "# TODO"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0    Transfer learning considers related but distin...\n",
              "1    Transfer learning uses relevant auxiliary data...\n",
              "2    The probabilistic serial (PS) rule is one of t...\n",
              "3    As the rapid growth of online social media att...\n",
              "4    In this paper, we study the low-rank tensor co...\n",
              "Name: abstract, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "18IQzkKOgJXH",
        "outputId": "933898c2-9990-4a3a-ee72-1ae0cabed0ba"
      },
      "source": [
        "from textblob import TextBlob\n",
        "\n",
        "def textblob_tokenizer(str_input):\n",
        "    blob = TextBlob(str_input.lower())\n",
        "    tokens = blob.words\n",
        "    words = [token.stem() for token in tokens]\n",
        "    return words\n",
        "\n",
        "vec = TfidfVectorizer(tokenizer=textblob_tokenizer,\n",
        "                      stop_words='english',\n",
        "                      use_idf=True)\n",
        "matrix = vec.fit_transform(aaai_data_clustering)\n",
        "df = pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())\n",
        "df"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/feature_extraction/text.py:385: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['abov', 'afterward', 'alon', 'alreadi', 'alway', 'ani', 'anoth', 'anyon', 'anyth', 'anywher', 'becam', 'becaus', 'becom', 'befor', 'besid', 'cri', 'describ', 'dure', 'els', 'elsewher', 'empti', 'everi', 'everyon', 'everyth', 'everywher', 'fifti', 'formerli', 'forti', 'ha', 'henc', 'hereaft', 'herebi', 'hi', 'howev', 'hundr', 'inde', 'latterli', 'mani', 'meanwhil', 'moreov', 'mostli', 'nobodi', 'noon', 'noth', 'nowher', 'onc', 'onli', 'otherwis', 'ourselv', 'perhap', 'pleas', 'seriou', 'sever', 'sinc', 'sincer', 'sixti', 'someon', 'someth', 'sometim', 'somewher', 'themselv', 'thenc', 'thereaft', 'therebi', 'therefor', 'thi', 'thu', 'togeth', 'twelv', 'twenti', 'veri', 'wa', 'whatev', 'whenc', 'whenev', 'wherea', 'whereaft', 'wherebi', 'wherev', 'whi', 'yourselv'] not in stop_words.\n",
            "  'stop_words.' % sorted(inconsistent))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.053638</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.103199</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.03918</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.057274</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.060785</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>397</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>398 rows × 4002 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     'crowd-sourc  'intrins  'mostli   's  ...    “    ”  ℓ1-regular  ﬁxed\n",
              "0             0.0       0.0      0.0  0.0  ...  0.0  0.0         0.0   0.0\n",
              "1             0.0       0.0      0.0  0.0  ...  0.0  0.0         0.0   0.0\n",
              "2             0.0       0.0      0.0  0.0  ...  0.0  0.0         0.0   0.0\n",
              "3             0.0       0.0      0.0  0.0  ...  0.0  0.0         0.0   0.0\n",
              "4             0.0       0.0      0.0  0.0  ...  0.0  0.0         0.0   0.0\n",
              "..            ...       ...      ...  ...  ...  ...  ...         ...   ...\n",
              "393           0.0       0.0      0.0  0.0  ...  0.0  0.0         0.0   0.0\n",
              "394           0.0       0.0      0.0  0.0  ...  0.0  0.0         0.0   0.0\n",
              "395           0.0       0.0      0.0  0.0  ...  0.0  0.0         0.0   0.0\n",
              "396           0.0       0.0      0.0  0.0  ...  0.0  0.0         0.0   0.0\n",
              "397           0.0       0.0      0.0  0.0  ...  0.0  0.0         0.0   0.0\n",
              "\n",
              "[398 rows x 4002 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Iq9qw7KXa7Xu",
        "outputId": "06a49715-599c-4922-ee69-a178b9a310c9"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "Sum_of_squared_distances = []\n",
        "K = range(2,7)\n",
        "for k in K:\n",
        "   km = KMeans(n_clusters=k, max_iter=200, n_init=10)\n",
        "   km = km.fit(matrix)\n",
        "   Sum_of_squared_distances.append(km.inertia_)\n",
        "\n",
        "plt.plot(K, Sum_of_squared_distances, 'bx-')\n",
        "plt.xlabel('k')\n",
        "plt.ylabel('Sum_of_squared_distances')\n",
        "plt.title('Elbow Method For Optimal k')\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "GOADHECuOatd",
        "outputId": "f71d1d05-c596-477c-8e66-bb881674e076"
      },
      "source": [
        "# number 4 and 5 look like an elbow point. \n",
        "#if go with 4 clusters\n",
        "number_of_clusters=4\n",
        "km = KMeans(n_clusters=number_of_clusters)\n",
        "km.fit(matrix)\n",
        "km.fit\n",
        "print(\"Top terms per cluster:\")\n",
        "order_centroids = km.cluster_centers_.argsort()[:, ::-1]\n",
        "terms = vec.get_feature_names()\n",
        "for i in range(number_of_clusters):\n",
        "    top_words = [terms[ind] for ind in order_centroids[i, :10]]\n",
        "    print(\"Cluster {}: {}\".format(i, ' '.join(top_words)))\n",
        "'''\n",
        "if go with 4 clusters, based on top words for each cluster, these clustering makes sense: \n",
        "cluster 0: related to user and language\n",
        "cluster 1: related to search and constraint\n",
        "cluster 2: doesn't make much sense as the words are all very general\n",
        "cluster 3: related to gaming and social networking\n",
        "'''"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Top terms per cluster:\n",
            "Cluster 0: game agent rule mechan prefer score vote energi elect voter\n",
            "Cluster 1: learn algorithm model data method thi propos approach problem use\n",
            "Cluster 2: user model social data recommend activ predict inform network method\n",
            "Cluster 3: plan problem search heurist robot bound algorithm thi planner action\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\nbased on top words for each cluster, these clustering makes sense: \\ncluster 0: related to user and language\\ncluster 1: related to search and constraint\\ncluster 2: doesn't make much sense as the words are all very general\\ncluster 4: related to gaming and social networking\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTrt2vPN07mt",
        "outputId": "ce0215a1-16c8-4347-bfb3-a06283f97455"
      },
      "source": [
        "#if go with 5 clusters\n",
        "number_of_clusters=5\n",
        "km = KMeans(n_clusters=number_of_clusters)\n",
        "km.fit(matrix)\n",
        "km.fit\n",
        "print(\"Top terms per cluster:\")\n",
        "order_centroids = km.cluster_centers_.argsort()[:, ::-1]\n",
        "terms = vec.get_feature_names()\n",
        "for i in range(number_of_clusters):\n",
        "    top_words = [terms[ind] for ind in order_centroids[i, :10]]\n",
        "    print(\"Cluster {}: {}\".format(i, ' '.join(top_words)))\n",
        "'''\n",
        "if go with 5 clusters, based on top words for each cluster, these clustering makes sense: \n",
        "cluster 0: related to social netsork\n",
        "cluster 1: related to language\n",
        "cluster 2: related to agent and gaming\n",
        "cluster 3: related to search and optimize in contraints\n",
        "cluster 4: related to classification\n",
        "'''"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Top terms per cluster:\n",
            "Cluster 0: model user method social propos predict algorithm network thi data\n",
            "Cluster 1: languag ontolog logic function model problem thi use semant algorithm\n",
            "Cluster 2: agent game mechan task thi rule algorithm problem alloc time\n",
            "Cluster 3: problem algorithm plan constraint search thi solv optim bound use\n",
            "Cluster 4: learn data label featur method propos domain approach classif imag\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}